erikvan

• Vanem, Erik; Dong, Sheng; Hauteclocque, Guillaume de; Johannessen, Thomas Berge; Zhu, Tingyao & Prpic-Orsic, Jasna [Show all 10 contributors for this article] (2026). Statistical Modeling of the Ocean Environment. Annual Review of Statistics and Its Application. ISSN 2326-8298. 13, p. 1–28. doi: https:/www.annualreviews.org/content/journals/statistics. Full text in Research Archive
• Liang, Qin; Han, Peihua; Vanem, Erik; Knutsen, Knut Erik & Zhang, Houxiang (2025). Dual-Perspective 1D Fully Convolutional Network for State of Health Prediction in Maritime Battery Systems Using Charge and Discharge Curves. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/iecon58223.2025.11221596. Full text in Research Archive
• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Lindqvist, Bo Henry; Vanem, Erik & Bin, Riccardo De (2025). Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process. Lifetime Data Analysis. ISSN 1380-7870. 31, p. 300–339. doi: 10.1007/s10985-025-09648-z. Full text in Research Archive
• Grindheim, Christian Alm; Stakkeland, Morten; Glad, Ingrid Kristine & Vanem, Erik (2025). Comparative Study of Machine Learning Methods for State of Health Estimation of Maritime Battery Systems. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 147(3), p. 1–15. doi: 10.1115/1.4065967. Full text in Research Archive Show summary

  This paper tests two data-driven approaches for predicting the state of health (SOH) of lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, non-sequential approaches are investigated and various models are tested: ridge, lasso, support vector regression, and gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature, and current. Second, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory, transformer, and temporal convolutional network. Finally, the various models and the two approaches are compared in terms of their SOH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches. Copyright © 2024 by ASME

• Bakdi, Azzeddine & Vanem, Erik (2025). Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 147(3). doi: 10.1115/1.4066198. Full text in Research Archive Show summary

  Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

• Vanem, Erik & Wang, Shuai (2025). Data-driven state of health and state of safety estimation for alternative battery chemistries — A comparative review focusing on sodium-ion and LFP lithium-ion batteries. Future Batteries. 5. doi: 10.1016/j.fub.2025.100033. Full text in Research Archive Show summary

  This paper presents a comprehensive survey on data-driven online estimation of state of health (SoH) for alternative battery chemistries for maritime applications, with a particular focus on LFP lithium-ion and sodium-ion types of batteries. In addition, the emerging concept of state of safety (SoS), a critical yet underexplored metric for maritime battery systems, is explored. Building on previous work on nickel–manganese–cobalt (NMC) lithium-ion batteries, this study evaluates the applicability of existing SoH estimation methodologies to alternative chemistries. The findings suggest that similar data-driven approaches, including empirical and semi-empirical methods, physics-based models, machine learning models, and hybrid approaches, can be employed across these chemistries. However, the methods require calibration, fine-tuning, and validation for each specific battery type. It is believed that SoS holds significant potential for maritime applications, provided it incorporates a relevant set of safety sub-functions with properly defined thresholds and warning criteria. Its integration into real-time monitoring systems appears feasible, given continuous measurement of relevant inputs. However, further research is recommended on how to best account for interdependencies between the various safety sub-function and correlations in the input data as well as how to account for the effect of degradation on SoS. Additionally, it seems reasonable to investigate whether some kind of memory could be incorporated in order to account for the experience of previous abusive conditions.

• Wang, Hong; Gramstad, Odin; Schär, Styfen; Marelli, Stefano & Vanem, Erik (2024). Comparison of probabilistic structural reliability methods for ultimate limit state assessment of wind turbines. Structural Safety. ISSN 0167-4730. 111, p. 1–16. doi: 10.1016/j.strusafe.2024.102502. Full text in Research Archive Show summary

  The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.

• Vanem, Erik; Fekhari, Elias; Dimitrov, Nikolay; Kelly, Mark; Cousin, Alexis & Guiton, Martin (2024). A JOINT PROBABILITY DISTRIBUTION FOR MULTIVARIATE WIND-WAVE CONDITIONS AND DISCUSSIONS ON UNCERTAINTIES. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 146(6). doi: 10.1115/1.4064498. Full text in Research Archive
• Vanem, Erik; Liang, Qin; Bruch, Maximilian; Bakdi, Azzeddine & Alnes, Øystein Åsheim (2024). Data-informed state of health estimation for maritime lithium-ion battery systems using an ensemble of simple linear models. Journal of Marine Engineering & Technology. ISSN 2046-4177. doi: 10.1080/20464177.2024.2440983. Full text in Research Archive Show summary

  Electric ships utilising batteries for onboard energy storage have increased in numbers in recent years. One safety aspect of such ships is that the batteries have sufficient capacity at all times during operation. It is therefore necessary to monitor the battery capacity and to display a measure of this to the navigator on the bridge. Classification societies require that the state of health from the BMS shall be verified by an independent method, and this is often obtained by regular physical tests. In this paper, a data-driven method based on sensor data is presented as an alternative approach to verifying the state of health. The method presented in this paper offers significant advantages in that it is a snapshot method, and that it does not require training data. It uses an ensemble of simple linear model based on Coulomb counting and it is tried out on both laboratory cycling data and operational data from several battery systems onboard ships in normal operation. Results are quite good on lab data, and promising on operational data, but proper validation of the approach is difficult without longer time-histories of operational data.

• Bakdi, Azzeddine; Bruch, Maximilian; Liang, Qin & Vanem, Erik (2024). Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems, Proceedings of the Annual Conference of the PHM Society 2024, Vol. 16 No. 1 (2024). PHM Society. ISSN 9781936263295. doi: 10.36001/phmconf.2024.v16i1.4135. Full text in Research Archive Show summary

  Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

• Clarindo, Guilherme; Soares, C Guedes & Vanem, Erik (2024). Modeling sea waves accounting for serial correlations and seasonal effects for the Aquçadoura offshore site test. In Soares, Carlos Guedes & Wang, Shan (Ed.), Innovations in Renewable Energies Offshore Proceedings of the 6th International Conference on Renewable Energies Offshore (RENEW 2024, 19-21 November 2024, Lisbon, Portugal). CRC Press. ISSN 9781032905570. p. 1045–1053. doi: 10.1201/9781003558859. Full text in Research Archive
• Vanem, Erik; Liang, Qin; Bruch, Maximilian; Bøthun, Gjermund; Bruvik, Katrine & Thorbjørnsen, Kristian [Show all 7 contributors for this article] (2024). Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships. Journal of Dynamics, Monitoring and Diagnostics. ISSN 2833-650X. 3(1), p. 11–20. doi: 10.37965/jdmd.2024.500. Full text in Research Archive Show summary

  Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion is growing. To ensure the safety of such ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require the state of health (SOH) to be verified by independent tests. This paper addresses statistical modelling of SOH for maritime lithium-ion batteries based on operational sensor data. Various methods for sensor-based, data-driven degradation monitoring will be presented, and advantages and challenges with the different approaches will be discussed. The different approaches include cumulative degradation models and snapshot models, models that need to be trained and models that need no prior training, and pure data-driven models and physics-informed models. Some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

• Liang, Qin; Knutsen, Knut Erik; Vanem, Erik; Æsøy, Vilmar & Zhang, Houxiang (2024). A review of maritime equipment prognostics health management from a classification society perspective. Ocean Engineering. ISSN 0029-8018. 301. doi: 10.1016/j.oceaneng.2024.117619. Full text in Research Archive
• Liang, Qin; Han, Peihua; Vanem, Erik; Knutsen, Knut Erik & Zhang, Houxiang (2024). A Hybrid Approach Integrating Physics-Based Models and Expert-Augmented Neural Networks for Ship Fuel Consumption Prediction. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 147(3). doi: 10.1115/1.4066945. Full text in Research Archive Show summary

  The International Maritime Organization’s recent approval of the 2023 strategy on reduction of greenhouse gas emissions amplifies the pressure on stakeholders to achieve net-zero emissions in shipping by 2050. Considering the anticipated predominance of traditional single-fuel engines into the next decade, due to their high efficiency and economic benefits, the implementation of operational measures stands as the foremost effective method for mitigating emissions and reducing fuel consumption. Accurate fuel consumption prediction is crucial for informed decision-making and operational efficiency. This paper introduces an innovative hybrid model, combining an advanced physics-based model with an expert-augmented neural network, offering superior fuel consumption predictions. Expert knowledge is integrated into the neural network model to enhance its learning capabilities. Performance is validated against DNV Navigator Insight and publicly available fuel consumption reporting data, demonstrating superiority over purely data-driven and physics-based models. This hybrid approach bridges accuracy and scalability for sustainable maritime operations.

• Vanem, Erik; Gramstad, Odin; Babanin, Alexander; Bin, Riccardo De & Trulsen, Karsten (2024). On the distribution of ocean wave crest heights in varying wave conditions. Journal of Ocean Engineering and Marine Energy. ISSN 2198-6444. 10(4), p. 797–815. doi: 10.1007/s40722-024-00350-0. Full text in Research Archive Show summary

  This paper addresses the statistical distribution of wave crest heights in ocean environments. This is much needed in several engineering applications including risk and reliability assessment of marine and coastal structures and is an important input for design of ocean structures. However, even though crest height distributions have received a lot of attention within both academia and the industry, current state-of-the-art models have notable shortcomings and do not describe the distribution of crest heights well in all sea states. In particular, commonly used models do not accurately describe the upper tail in certain unstable wave conditions. This will be addressed in this paper, which investigates in what sea-state conditions, established crest height distributions describe the data well and under what conditions, these models fail to fit well to the data. A large dataset of crest heights observed in different sea states is analyzed, and various goodness-of-fit tests are applied to determine when the different statistical models are appropriate. Then rejection rates of the various statistical models will be linked to selected sea-state parameters to identify wave conditions where alternative statistical models are needed. Results from this study indicate that the well-known Forristall distribution, which is known to capture second-order effects well, fails to perform well in some sea states. Different variants of the Tayfun–Fedele distribution are also analyzed, and results suggest that this represent a slight improvement. Somewhat surprisingly, sea-state parameters such as significant wave height (Hs), significant wave steepness (S1), and the Benjamin–Feir index (BFI) do not seem to be very influential on the probability of rejecting these distributions, even though empirical skewness and kurtosis play a significant role. The implications of this for describing the distribution of crest heights well in certain sea-state conditions are discussed.

• Sánchez-Pozo, Nadia N.; Vanem, Erik; Bloomfield, Hannah & Aizpurua, Jose Ignacio (2024). A probabilistic risk assessment framework for the impact assessment of extreme events on renewable power plant components. Renewable Energy. ISSN 0960-1481. 240. doi: 10.1016/j.renene.2024.122168. Full text in Research Archive Show summary

  Climate change is expected to worsen the frequency, intensity, and impacts of extreme weather events. Renewable energy sources (RESs) play a key role in the decarbonization process to decelerate climate change effects. However, extreme events pose a significant threat to renewable energy infrastructure. Accordingly, understanding the impacts of extremes on RESs becomes crucial to ensure the reliability of power grids. In this context, this research presents a novel probabilistic risk assessment framework to evaluate the degradation of wind turbine transformers (WTTs) and photovoltaic (PV) panels in the face of extreme weather conditions. The framework uses a Gaussian copula to model the joint probability of extreme events, effectively incorporating multivariate phenomena. Case studies involving WTTs and PV panels operated in different wind and solar power plants, illustrate the effectiveness of the proposed methodology, demonstrating its ability to capture the combined influence of different meteorological variables on degradation rates. These results underscore the potential of this framework to assess weather-related risks in renewable energy systems, thereby enhancing their resilience and reliability.

• Liang, Qin; Vanem, Erik; Knutsen, Knut Erik; Æsøy, Vilmar & Zhang, Houxiang (2024). Anomaly Detection of Marine Diesel Engines: A Novel Approach using Transformer Neural Networks for Reconstruction and Residual Analysis. International Journal of Prognostics and Health Management. 15(3). doi: 10.36001/ijphm.2024.v15i3.3853. Full text in Research Archive Show summary

  This paper presents a novel unsupervised approach for anomaly detection in marine diesel engines using a Transformer Neural Network based autoencoder (TAE) and residual analysis with Sequential Probability Ratio Test (SPRT) and Sum of Squares of Normalized Residuals (SSNR). This approach adeptly captures temporal dependencies within normal time-series data without the necessity for labeled failure data. To assess the performance of the proposed methodology, a dataset containing faulty data is generated under the same operational profile as the normal training data. The model undergoes training using normal data, after which the faulty data is reconstructed utilizing the trained model. Subsequently, SPRT and SSNR are used to analyze the residuals from the observed and reconstructed faulty data. Significant deviations surpassing a predefined threshold are identified as anomalous behavior. Additionally, this study explores various architectures of Transformer neural networks and other types of neural networks to conduct a comprehensive comparative analysis of the performance of the proposed approach. Insights and recommendations derived from the performance analysis are also presented, offering valuable information for potential users to leverage. The experimental results demonstrate that the proposed approach can accurately and efficiently detect anomalies in marine diesel engines. Therefore, this approach can be considered as a promising solution for early anomaly detection, leading to timely maintenance and repair, and preventing costly downtime. Also, avoid accidents with probability severe consequences.

• Crespo, Luis G.; Agrell, Christian & Vanem, Erik (2024). A Scenario Optimization Approach to the Prediction of Extreme Structural Responses to Environmental Loading, Proceedings of ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2024). Volume 2 : Structures, Safety, and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 9780791887790. doi: 10.1115/OMAE2024-131595. Full text in Research Archive
• Vanem, Erik; Lande, Øystein & Fekhari, Elias (2024). A Simulation Study on the Usefulness of the Bernstein Copula for Statistical Modeling of Metocean Variables, Proceedings of ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2024). Volume 2 : Structures, Safety, and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 9780791887790. doi: 10.1115/OMAE2024-121159. Full text in Research Archive Show summary

  Probabilistic modelling of relevant environmental variables are crucial for the safe design and operation of marine structures. Using metocean data, a joint model of several variables can be estimated, including their dependence structure. Often, a conditional model is assumed for this, but recently the non-parametric Bernstein copula has been suggested as an alternative tool to model such dependencies. As a non-parametric technique, it is very flexible and often provides excellent goodness-of-fit to data with different dependencies. However, non-parametric techniques are prone to over-fitting and generalizability might be challenging. Moreover, care should be taken when using such models for extrapolation. In this paper, a simple simulation study will be presented that has investigated the usefulness of the Bernstein copula in modeling joint metocean variables. First, data have been generated from a known parametric joint distribution model. Then, a joint model based on the Bernstein copula is fitted to a subset of these data. Data simulated from the Bernstein-based models are then compared to data from the initial model. A particular focus will be put on how the model captures the dependencies in the extremes. Copyright © 2024 by ASME, made available by permission.

• Han, Peihua; Liang, Qin; Vanem, Erik; Knutsen, Knut Erik & Zhang, Houxiang (2024). Assessing Helicopter Turbine Engine Health: A Simple Yet Robust Probabilistic Approach. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. ISSN 2325-0178. 16(1). doi: 10.36001/phmconf.2024.v16i1.4186. Full text in Research Archive Show summary

  This paper presents a data-driven approach for assessing the health of helicopter turbine engines, developed for the PHM North America 2024 Conference Data Challenge. The task involves both regression and classification to estimate the torque margin and classify engine health as either nominal or faulty. To quantify the reliability of predictions, probabilistic outputs are generated. We employ a two-stage model where the predicted torque margin serves as an input feature for health classification. For probabilistic torque margin estimation, we introduce an empirical error sampling method to generate torque margin samples, followed by a rule-based distribution selection scheme to evaluate the resulting distributions. For fault classification, logistic regression is used to provide confidence estimates, and we incorporate a score-optimized loss function during training to mitigate penalties for false negatives. Our approach demonstrates strong generalization to unseen assets, ranking 2nd in the competition with a score of 0.94, demonstrating its effectiveness in predicting health conditions and uncertainty for more informed helicopter engine management.

• Liang, Qin; Vanem, Erik; Knutsen, Knut Erik; Æsøy, Vilmar & Zhang, Houxiang (2023). Unsupervised Anomaly detection In Marine Diesel Engines Using Transformer Neural Networks and Residual Analysis, Proceedings of the Asia Pacific Conference of the Prognostics and Health Management Society 2023. PHM Society. ISSN 9781936263394. doi: 10.36001/phmap.2023.v4i1.3724. Full text in Research Archive
• Vanem, Erik (2023). Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 145(5), p. 1–14. doi: 10.1115/1.4056786. Full text in Research Archive Show summary

  Abstract This article presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, which compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-h significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021 has been analyzed. This article is an extension of a study presented at OMAE 2022 (OMAE2022-78795) and includes additional preprocessing of the data to account for seasonality and new results.

• Vanem, Erik; Bruch, Maximilian; Liang, Qin; Thorbjørnsen, Hans Kristian; Valøen, Lars Ole & Alnes, Øystein Åsheim (2023). Data-driven snapshot methods leveraging data fusion to estimate state of health for maritime battery systems. Energy Storage. 5(8). doi: 10.1002/est2.476. Full text in Research Archive Show summary

  Abstract The number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of these ships, it is important to monitor the capacity that can be stored in the batteries, and classification societies typically require that this can be verified by independent tests—annual capacity tests. However, this paper discusses data‐driven alternatives based on operational sensor data collected from the batteries. There are different strategies for such data‐driven state of health (SOH) estimation. Some approaches require full operational history of the batteries in order to predict SOH, and this may be impractical due to several reasons. Thus, methods that are able to give reliable estimation of SOH based on only snapshots of the data streams are more attractive from a practical point of view. In this paper, data‐driven snapshot methods are explored and applied to degradation data from battery cells cycled in different laboratory tests. Hence, data from different sources are fused together with the aim of achieving better predictions. The paper presents the battery data show how relevant features can be extracted from snapshots of the data and presents data‐driven models for SOH estimation. It is discussed how such methods could be utilized in a data‐driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for nearly 40% of the tested cells, although the fusion of data from different laboratory tests did not improve results significantly. Results are greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available.

• Vanem, Erik (2023). Analysing multivariate extreme conditions using environmental contours and accounting for serial dependence. Renewable Energy. ISSN 0960-1481. 202, p. 470–482. doi: 10.1016/j.renene.2022.11.033. Full text in Research Archive Show summary

  Accurate statistical description of extreme environmental conditions is needed for risk assessment and management of marine structures and is a crucial input to design of any structure that need to withstand loads from environmental forces. Such descriptions are essentially multivariate extreme value problems, where the environmental loads are due to concurrent extreme combinations of several environmental variables. Typically, in coastal and ocean engineering applications, the simultaneous joint behaviour of significant wave height and wave period is of particular interest and is needed to describe the wave loads on marine structures. Environmental contours are often used to explore the extreme wave loads, and essentially consider extreme combinations of simultaneous significant wave height and wave period, and are based on a joint statistical distribution fitted to relevant metocean data. However, typical applications of environmental contours do not account for temporal dependencies in the environmental variables, and this may lead to an overestimation of extreme conditions. In this paper, an approach for partially accounting for serial dependence in the construction of environmental contours is proposed, based on simulating time-series of a primary variable which preserves both its marginal distribution and auto-correlation structure. It is shown that this gives lower estimates of extreme environmental conditions compared to conventional application of environmental contours that do not account for serial dependence. Hence, more accurate description of the extreme environment can be available for design and construction of structures exposed to environmental loads.

• Vanem, Erik; Liang, Qin; Ferreira, Carla; Agrell, Christian; Karandikar, Nikita & Wang, Shuai [Show all 14 contributors for this article] (2023). DATA-DRIVEN APPROACHES TO DIAGNOSTICS AND STATE OF HEALTH MONITORING OF MARITIME BATTERY SYSTEMS, PROCEEDINGS OF THE ANNUAL CONFERENCE OF THE PHM SOCIETY 2023. PHM society. ISSN 9781936263295. doi: 10.36001/phmconf.2023.v15i1.3437. Full text in Research Archive Show summary

  Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) can be verified by independent tests. However, this paper addresses data-driven approaches to state of health monitoring of maritime battery systems based on operational sensor data. Results from various approaches to sensor-based, data-driven degradation monitoring of maritime battery systems will be presented, and advantages and challenges with the different methods will be discussed. The different approaches include cumulative degradation models and snapshot models. Some of the models need to be trained, whereas others need no prior training. Moreover, some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Different datasets have been used in order to explore the various methods, including public datasets, data from laboratory tests and operational data from ships in actual operation. Lessons learned from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2023). A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries. Journal of Power Sources. ISSN 0378-7753. 556. doi: 10.1016/j.jpowsour.2022.232429. Full text in Research Archive Show summary

  Lithium-ion batteries are a prominent technology for the electrification of the transport sector, which itself is a key measure towards the departure from fossil fuels. The “green shift” is taking place in the marine industry too, where the number of battery-powered vessels is fastly growing. In this case, monitoring the battery State of Health is essential more than ever to optimise battery use, promote safety, and ensure the coverage of ship power and energy demands. Classification societies typically require annual capacity tests for this purpose; however, the tests are disruptive, costly and time-consuming. As a consequence they are seldom, in addition to not being always fully reliable. We propose a novel alternative semi-supervised learning approach to estimate the State of Health of a lithium-ion battery system with no labelled data, starting from a minimal set of weakly labelled data from another similar system. The method is based on operational sensor data gathered from the battery, together with the battery State of Charge. Our results show that the procedure is valid, and the obtained estimates can be used to significantly progress in failure prevention, operational optimisation, and for planning batteries at the design stage.

• Aizpurua, Jose Ignacio; Knutsen, Knut Erik; Heimdal, Markus & Vanem, Erik (2023). Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics. Ocean Engineering. ISSN 0029-8018. 275. doi: 10.1016/j.oceaneng.2023.114153. Full text in Research Archive
• Liang, Qin; Vanem, Erik; Xue, Yongjian; Alnes, Øystein; Zhang, Heke & Lam, James [Show all 7 contributors for this article] (2023). Data-driven state of health monitoring for maritime battery systems–a case study on sensor data from ships in operation. Ships and Offshore Structures. ISSN 1744-5302. doi: 10.1080/17445302.2023.2211241. Full text in Research Archive
• Hauteclocque, Guillaume de; Mackay, Ed & Vanem, Erik (2022). Quantitative comparison of environmental contour approaches. Ocean Engineering. ISSN 0029-8018. 245. doi: 10.1016/j.oceaneng.2021.110374. Full text in Research Archive
• Guo, Bingjie; Liang, Qin; Tvete, Hans Anton; Brinks, Hendrik W. & Vanem, Erik (2022). Combined machine learning and physics-based models for estimating fuel consumption of cargo ships. Ocean Engineering. ISSN 0029-8018. 255. doi: 10.1016/j.oceaneng.2022.111435. Full text in Research Archive
• Garre, Luca; Guo, Bingjie; Gramstad, Odin & Vanem, Erik (2022). Application of the Tail Equivalent Linearization Method to wave bending moment and comparison with experimental data. Probabilistic Engineering Mechanics. ISSN 0266-8920. 67. doi: 10.1016/j.probengmech.2021.103174. Full text in Research Archive
• Vanem, Erik & Fazeres-Ferradosa, Tiago (2022). A truncated, translated Weibull distribution for shallow water sea states. Coastal Engineering. ISSN 0378-3839. 172. doi: 10.1016/j.coastaleng.2021.104077. Full text in Research Archive Show summary

  Reliable probabilistic information of the long-term ocean wave climate is important in many coastal and ocean engineering applications such as the design of marine and coastal structures. There is therefore a need to establish accurate models for the long-term distribution of relevant metocean variables. One of the most important wave parameters in marine design is the significant wave height, which is a measure of the severity of the sea state, and wave loads are typically described in terms of this and other variables. Hence, a lot of effort has been put on the proper probabilistic modelling of significant wave height. However, probability distributions proposed to model significant wave height have mostly focused on deep water conditions and less efforts have been put on shallow water conditions. This paper proposes a truncated, translated Weibull distribution for the long-time distribution of significant wave height in shallow waters and demonstrates that the model fit shallow water data quite well. In particular, the proposed model describes shallow-water wave data better than the commonly used 3-parameter Weibull distribution, which is often the preferred distribution for deep water locations.

• Bakka, Haakon Christopher; Rognebakke, Hanne Therese Wist; Glad, Ingrid Kristine; Haff, Ingrid Hobæk & Vanem, Erik (2022). Estimating the effect of biofouling on ship shaft power based on sensor measurements. Ship Technology Research. ISSN 0937-7255. 70(3), p. 209–221. doi: 10.1080/09377255.2022.2159108. Full text in Research Archive Show summary

  Marine biofouling on a ship's hull and propeller increases the resistance of the ship moving through water and reduces the propulsion efficiency of the ship. Estimating the effect of fouling is difficult, as the biomass is rarely measured. In this paper, we present a new data-driven model for the total shaft power use of a large containership, in order to estimate the unobserved effect of fouling. Due to the limitations of both physical models and machine learning models, we develop a Bayesian generalized additive model for our purpose. We discuss issues of representative training data for the model. Further, we subset and subsample the data to a representative sample. Models are compared by out-of-sample predictive quality, physical appropriateness, and through autocorrelation of residuals. The Bayesian generalized additive model combined with computational inference using integrated nested Laplace approximations gives a robust estimate of the biofouling effect over time. It also allows a decomposition of the total shaft power use into effects of speed, weather, and other conditions. This model can be used to understand the effectiveness and timing of different hull and propeller treatments.

• Vanem, Erik; Zhu, Tingyao & Babanin, Alexander (2022). Statistical modelling of the ocean environment – A review of recent developments in theory and applications. Marine Structures. ISSN 0951-8339. 86, p. 1–15. doi: 10.1016/j.marstruc.2022.103297. Full text in Research Archive Show summary

  Probabilistic modelling and statistical analysis of environmental conditions is important for the design and assessment of ships and other marine structures. It will give a necessary input to structural reliability assessments and risk analysis and provides a means to identify design conditions that structures are expected to withstand in their lifetime. In this paper, recent developments in statistical modelling of relevant metocean variables describing the environment at sea will be reviewed and presented. This includes a review of statistical modelling applied to such data, for example wave-parameters, but also some theoretical and methodological developments from other fields of applications will be reviewed. The paper is divided into different sub-sections addressing various aspects of statistical modelling of the ocean environment, such as long-term and short-term statistics, extreme value analysis, non-stationary analysis and covariate effects, multivariate analysis and joint distributions, spatial statistics and machine learning applications. This distinction into sub-topics may be somewhat arbitrary, and some papers address several of these topics, e.g. non-stationary, multivariate extreme value statistics for spatial data, but it is believed to be useful to still keep separate sections for the main aspects. It is believed that this overview of recent developments in statistical modelling of the ocean environment will be useful for anyone involved in design or risk assessment of marine structures, and that it may contribute to push the state of the art and industry practice.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). A novel semi-supervised learning approach for maritime lithium-ion battery monitoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. p. 85–90. Full text in Research Archive Show summary

  We propose a novel semi-supervised learning method to monitor the State of Health of lithium-ion batteries, a prominent technology for the electrification of the transport sector. Our approach enables State of Health monitoring of batteries with no labeled data, starting from a minimal set of labeled data from another similar battery. This can be achieved by exploiting the relation between a pseudo-capacity measure and the total capacity of the labeled data. Our results with operational data from maritime batteries show that the approach is valid and can lead to significant progress in failure prevention, operational optimization, and for planning batteries at the design stage.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage. ISSN 2352-152X. 52. doi: 10.1016/j.est.2022.104903. Full text in Research Archive Show summary

  Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery capacity degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying real physical processes. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite being characterised by simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are provided by the NASA Ames Prognostics Center of Excellence, and are characterised by varying loads which simulate dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent observed capacity measurement is known, the other is based only on the nominal capacity of the cell. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors ranging from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor to ageing acceleration is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.

• Vanem, Erik; Bruch, Maximilian; Liang, Qin; Gonzalez, David Vicente Reyes & Alnes, Øystein Åsheim (2022). A Data-Driven Snapshot Method for State of Health Modelling and Diagnostics of Maritime Battery Systems, Proceedings of the Annual Conference of the PHM Society 2022. PHM Society. ISSN 9781936263370. doi: 10.36001/phmconf.2022.v14i1.3154. Full text in Research Archive Show summary

  Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests - annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. There are different strategies for such data-driven diagnostics. Some approaches, referred to as cumulative damage models, require full operational history of the batteries in order to predict state of health, and this may be impractical due to several reason. Thus, snapshot methods that are able to give reliable estimation of state of health based on only snapshots of the data streams are attractive candidates for data-driven diagnostics of battery systems on board ships. In this paper, data-driven snapshot methods are explored and applied to a novel set of degradation data from battery cells cycled in laboratory tests. The paper presents the laboratory tests, the resulting battery data, shows how relevant features can be extracted from snapshots of the data and presents data-driven models for state of health prediction. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for 40% of the tested cells. This is greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. Nevertheless, improved accuracy is required for such snapshot methods to be recommended for ships in actual operation.

• Hafver, Andreas; Agrell, Christian & Vanem, Erik (2022). Environmental contours as Voronoi cells. Extremes. ISSN 1386-1999. 25(3), p. 451–486. doi: 10.1007/s10687-022-00437-7. Full text in Research Archive Show summary

  Environmental contours are widely used as basis for design of structures exposed to environmental loads. The basic idea of the method is to decouple the environmental description from the structural response. This is done by establishing an envelope of joint extreme values representing critical environmental conditions, such that any structure tolerating loads on this envelope will have a failure probability smaller than a prescribed value. Specifically, given an n-dimensional random variable X and a target probability of failure pe, an environmental contour is the boundary of a set B⊂Rn with the following property: For any failure set F⊂Rn, if F does not intersect the interior of B, then the probability of failure, P(X∈F), is bounded above by pe. We work under the assumption that failure sets are convex, which is relevant for many real-world applications. In this paper, we show that such environmental contours may be regarded as boundaries of Voronoi cells. This geometric interpretation leads to new theoretical insights and suggests a simple novel construction algorithm that guarantees the desired probabilistic properties. The method is illustrated with examples in two and three dimensions, but the results extend to environmental contours in arbitrary dimensions. Inspired by the Voronoi-Delaunay duality in the numerical discrete scenario, we are also able to derive an analytical representation where the environmental contour is considered as a differentiable manifold, and a criterion for its existence is established.

• Bakdi, Azzeddine & Vanem, Erik (2022). Fullest COLREGs Evaluation Using Fuzzy Logic for Collaborative Decision-Making Analysis of Autonomous Ships in Complex Situations. IEEE Transactions on Intelligent Transportation Systems (T-ITS). ISSN 1524-9050. 23(10), p. 18433–18445. doi: 10.1109/TITS.2022.3151826. Full text in Research Archive Show summary

  Maritime Autonomous Surface Ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; AI-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs. This fullest COLREGs evaluation is modelled in form of a fuzzy expert system based on ordinary seamanship practice. The full input space spans 21 features derived from maneuverability-dependent risk, AIS traffic data, vessel information, maps and nautical charts, water-depth, visibility, and sea conditions. The model assesses pairwise vessel encounters over the full time-window of a situation from entrance to exit. 42 fuzzy rules are designed in 6 criteria that represent COLREGs Rules 2–19 and model their logical connections, priorities, and relationships. This algorithmic COLREGs form satisfies the crucial needs in simulation, collision-avoidance, complexity monitoring, and compliance quantification in MASS applications. The fullest COLREGs evaluation model is verified on a large database of historical encounters using real data from multiple sources.

• Vanem, Erik (2022). Analyzing Extreme Sea State Conditions by Time-Series Simulation. In Ehlers, Sören (Eds.), International Conference on Ocean, Offshore and Arctic Engineering ASME 2022 41st. The American Society of Mechanical Engineers (ASME). ISSN 9780791885864. doi: 10.1115/OMAE2022-78795. Full text in Research Archive Show summary

  This paper presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, that compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-hourly significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021, has been analysed. Copyright © 2022 by ASME, made available by permission.

• Liang, Qin; Vanem, Erik; Knutsen, Knut Erik & Zhang, Houxiang (2022). Data-Driven Prediction of Ship Propulsion Power Using Spark Parallel Random Forest on Comprehensive Ship Operation Data, 2022 IEEE 17th International Conference on Control & Automation (ICCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665495721. p. 303–308. doi: 10.1109/ICCA54724.2022.9831854. Full text in Research Archive Show summary

  This paper aims to propose an efficient machine learning framework for maritime big data and use it to train a random forest model to estimate ships’ propulsion power based on ship operation data. The comprehensive data include dynamic operations, ship characteristics and environment. The details of data processing, model configuration, training and performance benchmarking will be introduced. Both scikit-learn and Spark MLlib were used in the process to find the best configuration of hyperparameters. With this combination, the search and training are much more efficient and can be executed on latest cloud-based solutions. The result shows random forest is a feasible and robust method for ship propulsion power prediction on large datasets. The best performing model achieved a R2 score of 0.9238.

• Vanem, Erik (2021). STATISTICAL SURROGATE MODELS FOR SHIP FATIGUE. In Das, Purnendu (Eds.), Proceedings of 3rd International Conference on Structural Integrity for Offshore Energy Industry. ASRANet. ISSN 9781838322632. p. 61–69. Full text in Research Archive
• Vanem, Erik; Alnes, Øystein Åsheim & Lam, James (2021). DATA-DRIVEN DIAGNOSTICS AND PROGNOSTICS FOR MODELLING THE STATE OF HEALTH OF MARITIME BATTERY SYSTEMS – A REVIEW, Proceedings of the Annual Conference of the PHM Society 2021. PHM Society. ISSN 9781936263356. doi: 10.36001/phmconf.2021.v13i1.2972. Full text in Research Archive Show summary

  Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

• Mackay, Ed; Hauteclocque, Guillaume de; Vanem, Erik & Jonathan, Philip (2021). The effect of serial correlation in environmental conditions on estimates of extreme events. Ocean Engineering. ISSN 0029-8018. 242. doi: 10.1016/j.oceaneng.2021.110092. Full text in Research Archive
• Vanem, Erik; Aarsnes, Lars Holterud; Storhaug, Gaute & Astrup, Ole Christian (2021). Statistical Modelling and Comparison of Model-Based Fatigue Calculations and Hull Monitoring Data for Container Vessels, Practical Design of Ships and Other Floating Structures. Proceedings of the 14th International Symposium, PRADS 2019, September 22-26, 2019, Yokohama, Japan- Volume II. Springer. ISSN 9789811546716. p. 517–536. doi: 10.1007/978-981-15-4672-3_33. Full text in Research Archive Show summary

  This paper presents the results of statistical analyses on the difference between measured and calculated fatigue rates for an ocean-going container vessel. Data from a hull monitoring system installed on-board this ship provides time-series of observed fatigue rates in actual operation, sea- and weather conditions. In addition, a hydrodynamic model has been used to calculate fatigue rates for selected locations in the mid-ship section due to vertical bending moment only, corresponding to experienced weather conditions over the same time period. The measured fatigue rates are then compared to the calculated fatigue rates and statistical regression models are established to explain the differences as a function of selected explanatory variables. This can then be used to correct for biases in the numerical models. Overall, the results indicate that the models perform reasonably well and are able to describe much of the variation in the difference. However, the models are found to not generalize very well and it may be challenging to find models for a whole fleet of ships.

• Vanem, Erik; Salucci, Clara Bertinelli; Bakdi, Azzeddine & Alnes, Øystein Åsheim (2021). Data-driven state of health modelling—A review of state of the art and reflections on applications for maritime battery systems. Journal of Energy Storage. ISSN 2352-152X. 43, p. 1–27. doi: 10.1016/j.est.2021.103158. Full text in Research Archive Show summary

  Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests — annual capacity tests. However, this paper discusses data-driven state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

• Leo, Francesco De; Besio, Giovanni; Briganti, Riccardo & Vanem, Erik (2021). Non-stationary extreme value analysis of sea states based on linear trends. Analysis of annual maxima series of significant wave height and peak period in the Mediterannean Sea. Coastal Engineering. ISSN 0378-3839. 167. doi: 10.1016/j.coastaleng.2021.103896. Full text in Research Archive Show summary

  Non-stationary Extreme Value Analysis (NEVA) allows to determine the probability of exceedance of extreme sea states taking into account trends in the time series of data at hand. In this work, we analyse the reliability of NEVA of significant wave height () and peak period () under the assumption of linear trend for time series of annual maxima (AM) in the Mediterranean Sea. A methodology to assess the significance of the results of the non-stationary model employed is proposed. Both the univariate long-term extreme value distribution of and the bivariate distribution of and are considered. For the former, a non-stationary Generalized Extreme Value (GEV) probability is used, and a methodology to compute the parameters of the distribution based on the use of a penalty function is explored. Then, non-stationary GEV is taken as a reference to compute the Environmental Countours of and , assuming a conditional model for the latter parameter. Several methods to compute linear trends are analysed and cross-validated on the series of AM at more than 20,000 hindcast nodes. Results show that the non-stationary analysis provides advantages over the stationary analysis only when all the considered metrics are consistent in indicating the presence of a trend. Moreover, both the univariate return levels of and bivariate return levels of and show a marked dependence to the time window considered in the GEV distribution formulation. Therefore, when applying NEVA for coastal and marine applications, the hypothesis of linear trend and the length of the reference data used for the non-stationary distribution should be carefully considered.

• Vestre, Arnstein; Bakdi, Azzeddine; Vanem, Erik & Engelhardtsen, Øystein (2021). AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres. Journal of navigation. ISSN 0373-4633. 74(5), p. 1–24. doi: 10.1017/S0373463321000357. Full text in Research Archive Show summary

  Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

• Haselsteiner, Andreas F.; Coe, Ryan G.; Manuel, Lance; Chai, Wei; Leira, Bernt Johan & Clarindo, Guilherme [Show all 22 contributors for this article] (2021). A benchmarking exercise for environmental contours. Ocean Engineering. ISSN 0029-8018. 236, p. 1–29. doi: 10.1016/j.oceaneng.2021.109504. Full text in Research Archive Show summary

  Environmental contours are used to simplify the process of design response analysis. A wide variety of contour methods exist; however, there have been a very limited number of comparisons of these methods to date. This paper is the output of an open benchmarking exercise, in which contributors developed contours based on their preferred methods and submitted them for a blind comparison study. The exercise had two components—one, focusing on the robustness of contour methods across different offshore sites and, the other, focusing on characterizing sampling uncertainty. Nine teams of researchers contributed to the benchmark. The analysis of the submitted contours highlighted significant differences between contours derived via different methods. For example, the highest wave height value along a contour varied by as much as a factor of two between some submissions while the number of metocean data points or observations that fell outside a contour deviated by an order of magnitude between the contributions (even for contours with a return period shorter than the duration of the record). These differences arose from both different joint distribution models and different contour construction methods, however, variability from joint distribution models appeared to be higher than variability from contour construction methods.

• Huseby, Arne; Vanem, Erik; Agrell, Christian & Hafver, Andreas (2021). Convex environmental contours. Ocean Engineering. ISSN 0029-8018. 235, p. 1–11. doi: 10.1016/j.oceaneng.2021.109366. Full text in Research Archive Show summary

  Environmental contours are widely used as a basis for e.g., ship design, especially in early design phases. The traditional approach to such contours is based on the well-known Rosenblatt transformation. Here we focus on convex contours estimated using Monte Carlo methods and establish a rigorous mathematical foundation for such contours. In the present paper we also present an improved simulation procedure based on importance sampling. In particular, we show how this procedure can be extended to cases with omission factors and where the joint distribution of the environmental variables is a discrete mixture. It is well-known that contours constructed using Monte Carlo simulation typically have certain irregularities. In particular, the sets bounded by the estimated contours appear to be convex. However, when the curves are investigated more closely, they include a large number of small loops. In the present paper we provide a precise condition for convexity, and propose a smoothing method which can be used to eliminate the loops. The methods are illustrated by a numerical example.

• Bakdi, Azzeddine; Glad, Ingrid Kristine & Vanem, Erik (2021). Testbed Scenario Design Exploiting Traffic Big Data for Autonomous Ship Trials Under Multiple Conflicts With Collision/Grounding Risks and Spatio-Temporal Dependencies. IEEE Transactions on Intelligent Transportation Systems (T-ITS). ISSN 1524-9050. 22(12). doi: 10.1109/TITS.2021.3095547. Full text in Research Archive Show summary

  Autonomous ships are promoted as the future of the maritime transport industry aiming to overcome conventional vessels in terms of performance, safety and environmental impact. Yet their tangled cyber-physical-social interactions and new emerging properties induce questions regarding their liability and trustworthiness. Digital simulations and sea trials are launched to assure the safety requirements and social expectations are met a priori. This paper presents the design of realistic testbed scenarios from huge historical data through a high-performance computational method to recommend a complete set of navigation scenarios for autonomy tests. The developed approach integrates traffic big data from Automatic Identification System (AIS) with high-resolution digital maps, vessel information registry, and digital nautical charts. All historical vessel-to-ground and vessel-to-vessel interactions are efficiently analyzed through a hierarchical method for collision and grounding conflicts assessment with a 15-minutes prediction horizon. Relative risk is evaluated accurately over full periods of predicted close-quarters situations subject to physical limits and sea-room availability for evasive maneuverers under COLREG rules and traffic separation schemes. Spatial dependencies among multiple conflicts define risky momentary traffic situations modelled through directed graph representation of nested interactions. Their temporal dependencies describe navigation scenarios through dynamic co-behaviors between multiple participating vessels over a period of time. Finally, we analyze negative/positive actions that increase/decrease the complexity. The presented algorithms are computationally very efficient, they scale to several (country*year)s where millions of scenarios are extracted, classified, and scored by their relative risk, complexity, and likelihood for firm post-test conclusions.

• Vanem, Erik (2020). Bivariate regional extreme value analysis for significant wave height and wave period. Applied Ocean Research. ISSN 0141-1187. 101. doi: 10.1016/j.apor.2020.102266. Full text in Research Archive
• Ross, Emma; Astrup, Ole Christian; Bitner-Gregersen, Elzbieta; Bunn, Nigel; Feld, Graham & Gouldby, Ben [Show all 11 contributors for this article] (2020). On environmental contours for marine and coastal design. Ocean Engineering. ISSN 0029-8018. 195:106194, p. 1–17. doi: 10.1016/j.oceaneng.2019.106194. Full text in Research Archive Show summary

  Environmental contours are used in structural reliability analysis of marine and coastal structures as an approximate means to locate the boundary of the distribution of environmental variables, and hence sets of environmental conditions giving rise to extreme structural loads and responses. Outline guidance concerning the application of environmental contour methods is given in recent design guidelines from many organisations. However there is lack of clarity concerning (a) the differences between approaches to environmental contour estimation reported in the literature, and (b) the relationship between the environmental contour, corresponding to some return period, and the extreme structural response for the same period. Hence there is uncertainty about precisely when environmental contours should be used, and how they should be used well. This article seeks to provide some assistance in understanding the fundamental issues regarding environmental contours and their use in structural reliability analysis. Approaches to estimating the joint distribution of environmental variables, and to estimating environmental contours based on that distribution, are described. Simple freely-available software for estimation of the joint distribution, and hence environmental contours, is illustrated. Extra assumptions required to relate the characteristics of environmental contours to structural failure are outlined. Alternative response-based methods not requiring environmental contours are summarised. The results of an informal survey of the metocean user community regarding environmental contours are presented. Finally, recommendations about when and how environmental contour methods should be used are made.

• Vanem, Erik; Guo, Bingjie; Ross, Emma & Jonathan, Philip (2020). Comparing different contour methods with response-based methods for extreme ship response analysis. Marine Structures. ISSN 0951-8339. 69. doi: 10.1016/j.marstruc.2019.102680. Full text in Research Archive
• Vanem, Erik; Hafver, Andreas & Nalvarte, Gladys (2020). Environmental contours for circular-linear variables based on the direct sampling method. Wind Energy. ISSN 1095-4244. 23(3), p. 563–574. doi: 10.1002/we.2442. Full text in Research Archive
• Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Engelhardtsen, Øystein (2020). AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain. Journal of Marine Science and Engineering (JMSE). 8(1). doi: 10.3390/jmse8010005. Full text in Research Archive Show summary

  The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

• Guo, Bingjie; Gramstad, Odin; Vanem, Erik & Bitner-Gregersen, Elzbieta (2019). Study on the Effect of Climate Change on Ship Responses Based on Nonlinear Simulations. Journal of Offshore Mechanics and Arctic Engineering. ISSN 0892-7219. 141(4). doi: 10.1115/1.4042182. Full text in Research Archive
• Vanem, Erik; Gramstad, Odin & Bitner-Gregersen, Elzbieta (2019). A simulation study on the uncertainty of environmental contours due to sampling variability for different estimation methods. Applied Ocean Research. ISSN 0141-1187. 91. doi: 10.1016/j.apor.2019.101870. Full text in Research Archive
• Vanem, Erik & Huseby, Arne (2019). Environmental Contours for Safe Design of Ships and Other Marine Structures, Book of Proceedings. 2nd International Conference on Structural Integrity for Offshore Energy Industry. ASRANET. ISSN 9781999614430. p. 70–77. Full text in Research Archive
• Huseby, Arne; Vanem, Erik & Barbosa, Maria Hjelset (2019). Environmental contours for mixtures of distributions. In Beer, Michael & Zio, Enrico (Ed.), Proceedings of the 29th European Safety and Reliability Conference(ESREL). 22 – 26 September 2019 Hannover, Germany. Research Publishing Services. ISSN 9789811127243. p. 839–846. doi: 10.3850/978-981-11-2724-30719-cd. Full text in Research Archive Show summary

  Environmental contours are widely used as a basis for e.g., ship design, especially in early design phases. The traditional approach to such contours is based on the well-known Rosenblatt transformation. In the present paper we present a numerical method making it possible to apply the inverse Rosenblatt transformation to mixtures of distributions. Due to the effects of this transformation the probabilistic properties of the resulting environmental contour can be distorted. Based on a precise definition of the concept of exceedance probability, valid for all types of environmental contours, we show how to evaluate a given contour and adjust it so that it gets the desired properties. The methods are illustrated by a numerical example.

• Steinbakk, Gunnhildur Högnadóttir; Aarsnes, Lars Holterud; Aldrin, Magne Tommy; Astrup, Ole Christian; Haug, Ola & Storhaug, Gaute [Show all 7 contributors for this article] (2019). Statistical Approximation to Synthetic Midship Hull Girder Stress Response. Journal of Ship Research. ISSN 0022-4502. 64(3), p. 266–277. doi: 10.5957/JOSR.02190008. Full text in Research Archive
• Vanem, Erik (2019). 3-dimensional environmental contours for structural reliability analysis of ships and offshore structures. Ships and Offshore Structures. ISSN 1744-5302. 14(1), p. 74–85. doi: 10.1080/17445302.2018.1478377. Full text in Research Archive
• Fazeres-Ferradosa, Tiago; Rosa-Santos, P.; Taveira-Pinto, Francisco; Vanem, Erik; Carvalho, H. & Correia, J. (2019). Editorial: Advanced research on offshore structures and foundation design: part 1. Proceedings of the Institution of Civil Engineers : Maritime Engineering. ISSN 1741-7597. 172(4), p. 118–123. doi: 10.1680/jmaen.2019.172.4.118. Full text in Research Archive
• Vanem, Erik; Fazeres-Ferradosa, Tiago; Rosa-Santos, P. & Taveira-Pinto, Francisco (2019). Statistical description and modelling of extreme ocean wave conditions. Proceedings of the Institution of Civil Engineers : Maritime Engineering. ISSN 1741-7597. 172(4), p. 124–132. doi: 10.1680/jmaen.2019.20. Full text in Research Archive
• Velarde, Joey; Vanem, Erik; Kramhøft, Claus & Sørensen, John Dalsgaard (2019). Probabilistic analysis of offshore wind turbines under extreme resonant response: Application of environmental contour method. Applied Ocean Research. ISSN 0141-1187. 93. doi: 10.1016/j.apor.2019.101947. Full text in Research Archive
• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2019). Efficient on-line anomaly detection for ship systems in operation. Expert Systems With Applications. ISSN 0957-4174. 121, p. 418–437. doi: 10.1016/j.eswa.2018.12.040. Full text in Research Archive Show summary

  We propose novel modifications to an anomaly detection methodology based on multivariate signal reconstruction followed by residuals analysis. The reconstructions are made using Auto Associative Kernel Regression (AAKR), where the query observations are compared to historical observations called memory vectors, representing normal operation. When the data set with historical observations grows large, the naive approach where all observations are used as memory vectors will lead to unacceptable large computational loads, hence a reduced set of memory vectors should be intelligently selected. The residuals between the observed and the reconstructed signals are analysed using standard Sequential Probability Ratio Tests (SPRT), where appropriate alarms are raised based on the sequential behaviour of the residuals. The modifications we introduce include: a novel cluster based method to select memory vectors to be considered by the AAKR, which gives an extensive reduction in computation time; a generalization of the distance measure, which makes it possible to distinguish between explanatory and response variables; and a regional credibility estimation used in the residuals analysis, to let the time used to identify if a sequence of query vectors represents an anomalous state or not, depend on the amount of data situated close to or surrounding the query vector. We demonstrate how the anomaly detection method and the proposed modifications can be successfully applied for anomaly detection on a set of imbalanced benchmark data sets, as well as on recent data from a marine diesel engine in operation.

• Vanem, Erik (2019). Environmental contours for describing extreme ocean wave conditions based on combined datasets. Stochastic environmental research and risk assessment (Print). ISSN 1436-3240. 33, p. 957–971. doi: 10.1007/s00477-019-01670-6. Full text in Research Archive
• Vanem, Erik & Guo, Bingjie (2019). Comparison of the Environmental Contour Method and Response-Based Analysis Using Response Emulator for Estimating Extreme Ship Responses, ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering (OMAE2019): Volume 4: Materials Technology. The American Society of Mechanical Engineers (ASME). ISSN 9780791858790. doi: 10.1115/OMAE2019-95098. Full text in Research Archive
• Ross, Emma; Astrup, Ole Christian; Bitner-Gregersen, Elzbieta; Bunn, Nigel; Feld, Graham & Gouldby, Ben [Show all 11 contributors for this article] (2019). On Environmental Contours for Marine and Coastal Design. In ASME, . (Eds.), ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. Volume 7B: Ocean Engineering. The American Society of Mechanical Engineers (ASME). ISSN 9780791858851. doi: 10.1115/OMAE2019-96587. Full text in Research Archive
• Vanem, Erik & Brandsæter, Andreas (2019). Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine. Journal of Marine Engineering & Technology. ISSN 2046-4177. 20(4), p. 217–234. doi: 10.1080/20464177.2019.1633223. Full text in Research Archive Show summary

  Sensor data from marine engine systems can be used to detect changes in performance in near real time which may be indicative of an impending failure. Thus sensor-based condition monitoring can be important for the reliability of ship machinery systems and improve maritime safety. However, there is a need for efficient and robust algorithms to detect such changes in the data streams. In this paper, sensor data from a marine diesel engine on an ocean-going ship are used for anomaly detection. The focus is on unsupervised methods based on clustering and the idea is to identify clusters in sensor data in normal operating conditions and to assess whether new observations belong to any of these clusters. The anomaly detection methods presented in this paper are applied to sensor data with no known faults. Being fully unsupervised, however, they do not rely on the assumption that all measurements are fault free as long as the amount of faulty data is small. The methods explored in this study include K-means clustering, Mixture of Gaussian models, density-based clustering, self-organising maps and support vector machines. These could be used separately or in combination to provide an efficient initial screening of the data and decide whether more detailed analysis is required. The performance of the various methods is generally found to be good, also in comparison with other methods. Overall, cluster-based methods are found to be promising candidates for online anomaly detection and condition monitoring of ship machinery systems based on sensor data.

• Fazeres-Ferradosa, Tiago; Taveira-Pinto, Francisco; Vanem, Erik; Reis, Maria Teresa & Neves, Luciana das (2018). Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications. Wind Engineering : The International Journal of Wind Power. ISSN 0309-524X. 42(4), p. 304–334. doi: 10.1177/0309524X18777323. Full text in Research Archive Show summary

  Joint statistical models for long-term wave climate are a key aspect of offshore wind engineering design. However, to find a joint model for sea-state characteristics is often difficult due to the complex nature of the wave climate and the physical constraints of sea-states phenomena. The available records of wave heights and periods are often very asymmetric in their nature. This article presents a copula-based approach to obtain the joint cumulative distribution function of the significant wave heights and the up-crossing mean period. This study is based on 124-month hindcast data concerning Horns Rev 3 offshore wind farm. The extra-parametrization technique of symmetric copulas is implemented to account for the asymmetry present in the data. The analysis of the total sea, the wind-sea and primary swell components is performed separately. The results show that the extra-parametrization technique with pairwise copulas consistently provided a better goodness-of-fit when compared to symmetric copulas. Moreover, it is demonstrated that the separation of the total sea into its components does not always improve the extra-parametrized copula’s performance. Furthermore, this article also discusses copulas application to offshore wind engineering.

• Brandsæter, Andreas & Vanem, Erik (2018). Ship speed prediction based on full scale sensor measurements of shaft thrust and environmental conditions. Ocean Engineering. ISSN 0029-8018. 162, p. 316–330. doi: 10.1016/j.oceaneng.2018.05.029. Full text in Research Archive Show summary

  The primary goal of this study is to adapt and validate various regression models to predict a ship's speed through water based on relevant and available full scale sensor measurements from a ship, including measurements of external environmental forces. The wind is measured by on-board wind sensors, and the effect of the waves is measured by motion reference units (MRUs) installed on the ship, measuring motions in six degrees of freedom; three translational motions and rotations about these. Accurate speed estimates, which relate directly to the estimates of the propulsion efficiency, fuel efficiency and pollution, are vital to be able to optimize ship design and operation. We demonstrate how regression models such as linear regression, projection pursuit (PPT) and generalized additive models (GAM) can be easily implemented for this application. A simple regression model based on the well-established relationship between ship speed and shaft thrust represent a benchmark model towards which the other models are compared.

• Vanem, Erik & Huseby, Arne (2018). Combined Long-Term and Short-Term Description of Extreme Ocean Wave Conditions by 3-Dimensional Environmental Contours, The Proceedings of The Twenty-eighth (2018) International OCEAN AND POLAR ENGINEERING CONFERENCE, ISOPE 2018. International Society of Offshore & Polar Engineers. ISSN 9781880653876. p. 470–477. Full text in Research Archive
• Fazeres-Ferradosa, Tiago; Taveira-Pinto, Francisco; Romao, X.; Vanem, Erik; Reis, Maria Teresa & Neves, Luciana das (2018). Probabilistic design and reliability analysis of scour protections for offshore windfarms. Engineering Failure Analysis. ISSN 1350-6307. 91, p. 291–305. doi: 10.1016/j.engfailanal.2018.04.035. Full text in Research Archive Show summary

  Scour protection is an important component of fixed bottom foundations for offshore wind turbines. Depending on the hydrodynamic conditions, they might be indispensable to avoid the structural collapse of the foundation due to scour phenomena. The design of scour protections is typically deterministic, which often results in overestimated mean diameters of the armour layer. Moreover, the design methodologies currently applied do not provide a measure of safety associated with the proposed design. The present research proposes a novel methodology to assess the safety of the protection and to perform the probabilistic design of static and dynamic scour protections. A case study based on Horns Rev. 3 offshore wind farm is used to show how to select the mean stone diameter according to a pre-defined probability of failure of the protection. The results show that a dynamic scour protection could be safely designed with a reduction of the mean stone diameter up to 15 cm, when compared with the statically stable protection.

• Vanem, Erik & Storvik, Geir Olve (2018). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data. International Journal of COMADEM. ISSN 1363-7681. 21(4), p. 7–18. Full text in Research Archive
• Vanem, Erik & Huseby, Arne (2018). Seasonal and Omni-Seasonal Environmental Contours for Extreme Sea States, Proceedings of the 7th International Maritime Conference on Design for Safety, DfS 2018. Osaka University. ISSN 9784908678127. p. 148–159. Full text in Research Archive
• Vanem, Erik & Brandsæter, Andreas (2018). Cluster-based anomaly detection in condition monitoring of a marine engine system, Proceedings of the 2018 Prognostics and System Health Management Conference (PHM-Chongqing 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538653807. p. 20–31. doi: 10.1109/PHM-Chongqing.2018.00011. Full text in Research Archive Show summary

  Sensor data can be used to detect changes in the performance of a system in near real-time which may be indicative of a system fault. However, there is a need for efficient and robust algorithms to detect such changes in the data streams. In this paper, sensor data from a marine diesel engine on an ocean-going ship are used for anomaly detection. The focus is on cluster-based methods for anomaly detection. The idea is to identify clusters in sensor data in normal operating conditions and to assess whether new observations belong in any of these clusters. New observations that obviously do not belong to any of the identified clusters, may be regarded as anomalies and call for further scrutiny. The cluster-based approaches to anomaly detection presented in this paper are truly unsupervised, and they are applied to sensor data with no known faults. Being fully unsupervised, however, the cluster based approaches do not need to explicitly assume that all observations in the training data are fault-free as long as the amount of faulty data is not large enough to form a separate cluster. Moreover, anomalies in the training data may be detected. Various clustering techniques are applied in this paper to provide a simple and unsupervised approach to anomaly detection. This could then be used as an efficient initial screening of the data streams before more detailed analysis is applied to suspicious parts of the data. The methods explored in this study include K-means clustering, Mixture of Gaussian models, density based clustering, self-organizing maps and spectral clustering. The performance of the various methods is reported, and also compared with that of other methods recently proposed for anomaly detection such as auto associative kernel regression (AAKR) and dynamical linear models (DLM). Overall, cluster-based methods are found to be promising candidates for online anomaly detection based on sensor data.

• Stefanakos, Christos & Vanem, Erik (2018). Comparison of wind and wave extremes in very long-term climatic scales, ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering - Volume 7B: Ocean Engineering. The American Society of Mechanical Engineers (ASME). ISSN 9780791851272. doi: 10.1115/OMAE2018-77581. Full text in Research Archive Show summary

  The study of very long-term ocean climate is of great interest in a number of different applications. In a climate change perspective, estimations of return values of wind and wave parameters to a future climate are of great importance for risk management and adaptation purposes. However, there are various ways of estimating the required return values, which introduce additional uncertainties in extreme weather and climate variables pertaining to both current and future climates. The different approaches that are considered in the present work include the annual maxima approach, the block maxima approach, and the MENU method which is based on the calculation of return periods of various level values from nonstationary time series data. Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained for a field of datapoints. Long-term datasets for an area in the North Atlantic Ocean are used in the present study, derived for project ExWaCli, comprising of 30 years in the present (historic period) and two sets of 30 years in the future (future projections). The comparison between the results of the various approaches reveals a variability of the return period estimates, and an assessment of this is given. Moreover, it seems that a slight shift towards higher extremes in a future wave climate might be possible based on the particular datasets that have been analysed.

• Vanem, Erik (2018). A simple approach to account for seasonality in the description of extreme ocean environments. Marine Systems & Ocean Technology. ISSN 1679-396X. 13, p. 63–73. doi: 10.1007/s40868-018-0046-6. Full text in Research Archive
• Bitner-Gregersen, Elzbieta M.; Vanem, Erik; Gramstad, Odin; Hørte, Torfinn; Aarnes, Ole Johan & Reistad, Magnar [Show all 9 contributors for this article] (2018). Climate change and safe design of ship structures. Ocean Engineering. ISSN 0029-8018. 149, p. 226–237. doi: 10.1016/j.oceaneng.2017.12.023. Full text in Research Archive Show summary

  The paper addresses projected changes of wave climate in the North Atlantic and their impact on the safe design of ships, with a particular focus given on associated uncertainties. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) uses four scenarios for future greenhouse gas concentrations in the atmosphere called Representative Concentration Pathways (RCP). Two of these scenarios are applied to investigate how sensitive the future North Atlantic wave climate is to the emissions they represent. Winds obtained from six global climate models have been used to simulate waves for a historical period at the end of last century and to project waves for a future period towards the end of this century for these two scenarios. Based on these projections, possible changes in extreme wind and waves are investigated and the associated uncertainties are discussed. The occurrence of rogue-prone sea states which may trigger generation of rogue waves in the past and future climate is also studied. It is shown how the scientific findings on uncertainties related to climate change projections and rogue waves can be incorporated in the risk-based approach used in current design practice of tankers, and ship structures in general. The potential effect of climate change on the safety level of current design practice for tankers is demonstrated. Finally, the paper discusses how structural design of ships can be upgraded to account for climate change and rogue waves without necessarily leading to significant economic consequences.

• Guo, Bingjie; Gramstad, Odin; Vanem, Erik & Bitner-Gregersen, Elzbieta (2018). Study on the Effect of Climate Change on Ship Responses based on Nonlinear Simulations. International Conference on Offshore Mechanics and Arctic Engineering (OMAE) [proceedings]. ISSN 1523-651X. doi: 10.1115/1.4042182. Full text in Research Archive
• Gramstad, Odin; Vanem, Erik & Bitner-Gregersen, Elzbieta (2018). Uncertainty of Environmental Contours due to Sampling Variability. International Conference on Offshore Mechanics and Arctic Engineering (OMAE) [proceedings]. ISSN 1523-651X. doi: 10.1115/omae2018-77810. Full text in Research Archive
• Stefanakos, Christos & Vanem, Erik (2018). Nonstationary fuzzy forecasting of wind and wave climate in very long-term scales. Journal of Ocean Engineering and Science. ISSN 2468-0133. 3, p. 144–155. doi: 10.1016/j.joes.2018.04.001. Full text in Research Archive Show summary

  Global climate change may have serious impact on human activities in coastal and other areas. Climate change may affect the degree of storminess and, hence, change the wind-driven ocean wave climate. This may affect the risks associated with maritime activities such as shipping and offshore oil and gas. So, there is a recognized need to understand better how climate change will affect such processes. Typically, such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections. This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations. According to this, fuzzy inference models (FIS) are coupled with nonstationary time series modelling, providing us with less biased climatic estimates. Two long-term datasets for an area in the North Atlantic Ocean are used in the present study, namely NORA10 (57 years) and ExWaCli (30 years in the present and 30 years in the future). Two distinct experiments have been performed to simulate future values of the time series in a climatic scale. The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.

• Vanem, Erik (2018). Statistical methods for condition monitoring systems. International Journal of Condition Monitoring. 8(1), p. 9–23. doi: 10.1784/204764218822441960. Full text in Research Archive
• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2017). Cluster Based Anomaly Detection with Applications in the Maritime Industry. In Li, Chuan; Oliveira, José Valente de; Ding, Ping; Guo, Wei; Shi, Juanjuan & Bai, Yun (Ed.), Proceedings of the 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control SDPC 2017. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781509040209. p. 328–333. doi: 10.1109/SDPC.2017.69. Full text in Research Archive
• Vanem, Erik; Brandsæter, Andreas & Gramstad, Odin (2017). Regression models for the effect of environmental conditions on the efficiency of ship machinery systems. In Walls, Lesley; Revie, Matthew & Bedford, Tim (Ed.), Risk, Reliability and Safety: Innovating Theory and Practice : Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016). CRC Press. ISSN 9781138029972. doi: 10.1201/9781315374987-57. Full text in Research Archive Show summary

  This book is a collection of papers which innovate the theory, method and practice of the safety, reliability and risk management of technological and engineered systems. Innovation is all about bringing change and improvements, often to something established. All papers have been contributed to the 26th European Safety and Reliability (ESREL) conference, an established meeting of scientists, academics and practitioners held annually under the auspices of the European Safety and Reliability Association (ESRA).The papers contributed to ESREL 2016 continue its tradition of sharing new ideas, methods and applications across multiple industry domains. The form of innovation varies across topics with relatively incremental change associated with those better understood problems and more radical innovations proposed for emergent problems or modelling challenges facing industry and society. This book contains papers on long-standing topics including risk management, reliability modelling, maintenance optimisation, probabilistic risk assessment, human factors, and more, as well as papers on contemporary themes, such as cyber-security and systems resilience. Practitioner authored papers provide insights into the challenges faced in real applications and share insights into the impact of the interventions to manage risk, while scientific papers share theoretical, methodological and empirical research contributions.

• Vanem, Erik (2017). Regional Frequency Analysis of Extreme Waves in a Changing Climate, Proceedings of the IEEE OCEANS 2017. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9780692935590. doi: 10.1016/j.oceaneng.2017.08.027. Full text in Research Archive
• Aarnes, Ole Johan; Reistad, Magnar; Breivik, Øyvind; Bitner-Gregersen, Elzbieta M.; Eide, Lars Ingolf & Gramstad, Odin [Show all 9 contributors for this article] (2017). Projected changes in significant wave height toward the end of the 21st century: Northeast Atlantic. Journal of Geophysical Research (JGR): Oceans. ISSN 2169-9275. 122(4), p. 3394–3403. doi: 10.1002/2016JC012521. Full text in Research Archive Show summary

  Wind field ensembles from six CMIP5 models force wave model time slices of the northeast Atlantic over the last three decades of the 20th and the 21st centuries. The future wave climate is investigated by considering the RCP4.5 and RCP8.5 emission scenarios. The CMIP5 model selection is based on their ability to reconstruct the present (1971–2000) extratropical cyclone activity, but increased spatial resolution has also been emphasized. In total, the study comprises 35 wave model integrations, each about 30 years long, in total more than 1000 years. Here annual statistics of significant wave height are analyzed, including mean parameters and upper percentiles. There is general agreement among all models considered that the mean significant wave height is expected to decrease by the end of the 21st century. This signal is statistically significant also for higher percentiles, but less evident for annual maxima. The RCP8.5 scenario yields the strongest reduction in wave height. The exception to this is the north western part of the Norwegian Sea and the Barents Sea, where receding ice cover gives longer fetch and higher waves. The upper percentiles are reduced less than the mean wave height, suggesting that the future wave climate has higher variance than the historical period.

• Vanem, Erik & Storvik, Geir Olve (2017). Anomaly detection using dynamical linear models and sequential testing on a marine engine system, PHM 2017 Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017. PHM Society. ISSN 9781936263264. p. 185–200. Full text in Research Archive
• Vanem, Erik (2017). On the Influence of Environmental Contour Method in Estimating Extreme Structural Response, ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering - Volume 3A: Structures, Safety and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 9780791857656. doi: 10.1115/OMAE2017-61047. Full text in Research Archive
• Vanem, Erik (2017). A regional extreme value analysis of ocean waves in a changing climate. Ocean Engineering. ISSN 0029-8018. 144, p. 277–295. doi: 10.1016/j.oceaneng.2017.08.027. Full text in Research Archive
• Huseby, Arne; Vanem, Erik & Eskeland, Karoline (2017). Evaluating properties of environmental contours. In Cepin, Marko & Bris, Radim (Ed.), Safety & Reliability, Theory and Applications. CRC Press. ISSN 9781138629370. p. 2101–2109. doi: 10.1201/9781315210469-265. Full text in Research Archive Show summary

  Environmental contours are widely used as a basis for e.g., ship design. The traditional approach to environmental contours is based on the well-known Rosenblatt transformation. However, due to the effects of this transformation the probabilistic properties of the resulting environmental contour can be difficult to interpret. An alternative approach to environmental contours uses Monte Carlo simulations on the joint environmental model, and thus obtain a contour without the need for the Rosenblatt transformation. This contour have well-defined probabilistic properties, but may sometimes be overly conservative in certain areas. In this paper we give a precise definition of the concept of exceedence probability which is valid for all types of environmental contours. Moreover, we show how to estimate the exceedence probability of a given environmental contour, and use this to compare different approaches to contour construction. The methods are illustrated by numerical examples based on real-life data.

View all works in NVA

• Steinbakk, Gunnhildur Högnadóttir; Aarsnes, Lars Holterud; Aldrin, Magne Tommy; Astrup, Ole Christian; Haug, Ola & Storhaug, Gaute [Show all 7 contributors for this article] (2019). Statistical approximation to synthetic mid-ship hull girder stress response. Full text in Research Archive
• Huseby, Arne; Vanem, Erik & Barbosa, Maria Hjelset (2019). Evaluating probabilistic properties of environmental contours for mixtures of distributions. Full text in Research Archive Show summary

  Environmental contours are widely used as a basis for e.g., ship design, especially in early design phases. The traditional approach to such contours is based on the well-known Rosenblatt transformation. In the present paper we present a numerical method making it possible to apply the inverse Rosenblatt transformation to mixtures of distributions. Due to the effects of this transformation the probabilistic properties of the resulting environmental contour can be distorted. Based on a precise definition of the concept of exceedance probability, valid for all types of environmental contours, we show how to evaluate a given contour and adjust it so that it gets the desired properties. The methods are illustrated by a numerical example.

• Vanem, Erik & Brandsæter, Andreas (2019). Statistical approaches to Anomaly Detection based on Real-Time Sensor Measurements. Full text in Research Archive
• Vanem, Erik (2019). Regional Frequency Analysis for spatial extreme value analysis of ocean waves. Full text in Research Archive
• Vanem, Erik & Huseby, Arne (2019). Environmental contours for safe design of ships and other marine structures. Full text in Research Archive
• Stefanakos, Christos & Vanem, Erik (2018). Comparison of Wind and Wave Extremes in Very Long-Term Climatic Scales. Full text in Research Archive
• Huseby, Arne; Vanem, Erik & Eskeland, Karoline (2017). On the evaluation of probabilistic properties of environmental contours. Full text in Research Archive Show summary

  Environmental contours are widely used as a basis for e.g., ship design. The traditional approach to environmental contours is based on the well-known Rosenblatt transformation. However, due to the effects of this transformation the probabilistic properties of the resulting environmental contour can be difficult to interpret. An alternative approach to environmental contours uses Monte Carlo simulations on the joint environmental model, and thus obtain a contour without the need for the Rosenblatt transformation. This contour have well-defined probabilistic properties, but may sometimes be overly conservative in certain areas. In this paper we give a precise definition of the concept of exceedence probability which is valid for all types of environmental contours. Moreover, we show how to estimate the exceedence probability of a given environmental contour, and use this to compare different approaches to contour construction. The methods are illustrated by numerical examples based on real-life data.

• Vanem, Erik (2017). Dynamical Linear Models Applied to Ship Sensor Data. DNV GL. ISSN 9788251503204. Full text in Research Archive

View all works in NVA