Erik Vanem

• 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.
• 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.
• 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 1-936263-39-4. doi: 10.36001/phmap.2023.v4i1.3724.
• 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 978-1-936263-29-5. doi: 10.36001/phmconf.2023.v15i1.3437. Full text in Research Archive
• 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. ISSN 2578-4862. 5(8). doi: 10.1002/est2.476. 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.
• 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.
• 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
• 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
• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (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
• 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.

• 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 conference proceedings. ISSN 978-1-6654-9572-1. p. 303–308. doi: 10.1109/ICCA54724.2022.9831854. 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.
• 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 (Print). ISSN 1524-9050. 23(10), p. 18433–18445. doi: 10.1109/TITS.2022.3151826. Full text in Research Archive
• 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
• 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 978-1-936263-37-0. doi: https:/doi.org/10.36001/phmconf.2022.v14i1.3154. Full text in Research Archive
• 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
• 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
• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (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 978-88-5511-309-0. 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.

• Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (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.

• 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.
• 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
• de Hauteclocque, Guillaume; Mackay, Ed & Vanem, Erik (2022). Quantitative comparison of environmental contour approaches. Ocean Engineering. ISSN 0029-8018. 245. doi: 10.1016/j.oceaneng.2021.110374.
• 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.
• 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
• Mackay, Ed; de Hauteclocque, Guillaume; 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.
• Vanem, Erik; Bertinelli Salucci, Clara; 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
• 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
• 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 (Print). ISSN 1524-9050. 22(12). doi: 10.1109/TITS.2021.3095547. 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.

• 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
• 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.

• De Leo, Francesco; 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
• 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 978-981-15-4671-6. p. 517–536. doi: https:/doi.org/10.1007/978-981-15-4672-3_33. Full text in Research Archive
• 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.
• 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.
• 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
• 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). ISSN 2077-1312. 8(1). doi: 10.3390/jmse8010005. Full text in Research Archive
• 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.
• 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.
• 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.
• 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.
• 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 978-1-9996144-3-0. p. 70–77.
• 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 978-0-7918-5885-1. doi: https:/doi.org/10.1115/OMAE2019-96587.
• 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 978-0-7918-5879-0. doi: https:/doi.org/10.1115/OMAE2019-95098.
• 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
• Velarde, Joey; Vanem, Erik; Kramhøft, Claus & Dalsgaard Sørensen, John (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.
• 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 978-981-11-2724-3. p. 839–846. doi: 10.3850/978-981-11-2724-3%200719-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.

• 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.
• 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.
• 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.
• 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
• 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.
• 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 978-1-5386-5380-7. p. 20–31. doi: 10.1109/PHM-Chongqing.2018.00011. Full text in Research Archive
• 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.
• 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.
• 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 978-0-7918-5127-2. 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 & 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 978-4-908678-12-7. p. 148–159.
• 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.
• 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.
• Fazeres-Ferradosa, Tiago; Taveira-Pinto, Francisco; Vanem, Erik; Reis, Maria Teresa & das Neves, Luciana (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
• Fazeres-Ferradosa, Tiago; Taveira-Pinto, Francisco; Romao, X.; Vanem, Erik; Reis, Maria Teresa & das Neves, Luciana (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
• 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 978-1-880653-87-6. p. 470–477.
• 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
• 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.

• 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
• Vanem, Erik (2018). Statistical methods for condition monitoring systems. International Journal of Condition Monitoring. ISSN 2047-6426. 8(1), p. 9–23. doi: 10.1784/204764218822441960.
• Brandsæter, Andreas; Vanem, Erik & Glad, Ingrid Kristine (2017). Cluster Based Anomaly Detection with Applications in the Maritime Industry. In Li, Chuan; de Oliveira, José Valente; 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 978-1-5090-4020-9. p. 328–333. doi: 10.1109/SDPC.2017.69. 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 978-0-7918-5765-6. doi: 10.1115/OMAE2017-61047.
• 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 978-1-936263-26-4. p. 185–200.
• Vanem, Erik (2017). Regional Frequency Analysis of Extreme Waves in a Changing Climate, Proceedings of the IEEE OCEANS 2017. IEEE conference proceedings. ISSN 978-0-692-93559-0. doi: 10.1016/j.oceaneng.2017.08.027.
• 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.
• Vanem, Erik (2017). A comparison study on the estimation of extreme structural response from different environmental contour methods. Marine Structures. ISSN 0951-8339. 56, p. 137–162. doi: 10.1016/j.marstruc.2017.07.002.
• 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 978-1138629370. 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.

• 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
• 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. 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.

• Brandsæter, Andreas; Manno, Gabriele; Vanem, Erik & Glad, Ingrid Kristine (2016). An Application of Sensor-Based Anomaly Detection in the Maritime Industry. In NA, NA (Eds.), 2016 IEEE International Conference on Prognostics and Health Management (ICPHM 2016). Curran Associates, Inc.. ISSN 9781509003839. doi: 10.1109/icphm.2016.7811910.
• Huseby, Arne; Vanem, Erik & Natvig, Bent (2015). Alternative environmental contours for structural reliability analysis. Structural Safety. ISSN 0167-4730. 54, p. 32–45. doi: 10.1016/j.strusafe.2014.12.003. Show summary

  This paper presents alternative methods for constructing environmental contours for probabilistic struc- tural reliability analysis of structures exposed to environmental forces such as wind and waves. For such structures, it is important to determine the environmental loads to apply in structural reliability calcula- tions and structural design. The environmental contour concept is an effective, risk-based approach in establishing such design conditions. Traditionally, such contours are established by way of a Rosenblatt transformation from the environmental parameter space to a standard normal space, which introduces uncertainties and may lead to biased results. The proposed alternative approach, however, eliminates the need for such transformations and established environmental contours based on direct Monte Carlo sampling from the joint distribution of the relevant environmental parameters. In this paper, three alter- native implementations of the proposed generic approach will be outlined.

• Vanem, Erik; Huseby, Arne & Natvig, Bent (2014). Bayesian hierarchical spatio-temporal modelling of trends and future projections in the ocean wave climate with a CO2 regression component. Environmental and Ecological Statistics. ISSN 1352-8505. 21(2), p. 189–220. doi: 10.1007/s10651-013-0251-6.
• Huseby, Arne; Vanem, Erik & Natvig, Bent (2013). A new approach to environmental contours for ocean engineering applications based on direct Monte Carlo simulations. Ocean Engineering. ISSN 0029-8018. 60, p. 124–135. doi: 10.1016/j.oceaneng.2012.12.034.
• Vanem, Erik; Natvig, Bent; Huseby, Arne & Bitner-Gregersen, E. M. (2013). An Illustration of the Effect of Climate Change on the Ocean Wave Climate - A Stochastic Model. In Singh, Bharat Raj (Eds.), Climate Change - Realities, Impacts Over Ice Cap, Sea Level and Risks. IntechOpen. ISSN 978-953-51-0934-1. p. 481–508. doi: 10.5772/54769.
• Huseby, Arne; Vanem, Erik & Natvig, Bent (2013). A New Method for Environmental Contours in Marine Structural Design. In ASME, conference (Eds.), Proceedings of ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2013). Volume 2A: Structures, Safety and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 978-0-7918-5532-4. doi: 10.1115/omae2013-10053.
• Vanem, Erik & Bitner-Gregersen, E. M. (2013). Modelling Long-Term Trends in Significant Wave Height and its Potential Impacts on Ship Structural Loads. In ASME, conference (Eds.), Proceedings of ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2013). Volume 2A: Structures, Safety and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 978-0-7918-5532-4. doi: 10.1115/omae2013-10023.
• Vanem, Erik & Walker, Sam-Erik (2013). Time Series Analysis of Significant Wave Height Data for Identification of Trends in the Ocean Wave Climate. In ASME, conference (Eds.), Proceedings of ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2013). Volume 2A: Structures, Safety and Reliability. The American Society of Mechanical Engineers (ASME). ISSN 978-0-7918-5532-4. doi: 10.1115/omae2013-10024.
• Vanem, Erik & Breivik, Olav Nikolai (2013). Bayesian hierarchical modelling of North Atlantic windiness. Natural Hazards and Earth System Sciences. ISSN 1561-8633. 13(3), p. 545–557. doi: 10.5194/nhess-13-545-2013. Show summary

  Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

• Vanem, Erik & Walker, Sam-Erik (2013). Identifying trends in the ocean wave climate by time series analyses of significant wave height data. Ocean Engineering. ISSN 0029-8018. 61, p. 148–160. doi: 10.1016/j.oceaneng.2012.12.042.
• Vanem, Erik (2012). A Stochastic Model for Long-Term Trends in Significant Wave Height with a CO2 Regression Component. In OMAE 2012, . (Eds.), Proceedings of the ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering OMAE2012 Volume I. The American Society of Mechanical Engineers (ASME). ISSN 9780791844885. p. 1–10. doi: 10.1115/omae2012-83012.
• Vanem, Erik; Natvig, Bent & Huseby, Arne (2012). Modelling the Effect of Climate Change on the Ocean Wave Climate Around the World, The Proceedings of the Tenth (2012) ISOPE Pacific/Asia Offshore Mechanics Symposium PACOMS-2012. International Society of Offshore & Polar Engineers. ISSN 978-1-880653-93-7. p. 145–152. doi: 10.1007/s12601-012-0013-7.
• Vanem, Erik; Natvig, Bent & Huseby, Arne (2012). Modelling the effect of climate change on the wave climate of the world’s oceans. Ocean Science Journal. ISSN 1738-5261. 47(2), p. 123–145. doi: 10.1007/s12601-012-0013-7.

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• Vanem, Erik (2013). Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Springer. ISBN 978-3-642-30252-7. 280 p.

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• Vanem, Erik (2019). Regional Frequency Analysis for spatial extreme value analysis of ocean waves.
• 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.
• Huseby, Arne; Vanem, Erik & Barbosa, Maria Hjelset (2019). Evaluating probabilistic properties of environmental contours for mixtures of distributions. 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 & Huseby, Arne (2019). Environmental contours for safe design of ships and other marine structures.
• Vanem, Erik & Brandsæter, Andreas (2019). Statistical approaches to Anomaly Detection based on Real-Time Sensor Measurements.
• Stefanakos, Christos & Vanem, Erik (2018). Comparison of Wind and Wave Extremes in Very Long-Term Climatic Scales.
• Huseby, Arne; Vanem, Erik & Eskeland, Karoline (2017). On the evaluation of probabilistic properties of environmental contours. 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 (2016). Extreme value analysis of ocean waves in a changing climate.
• Vanem, Erik; Glad, Ingrid Kristine & Storvik, Geir Olve (2016). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data.
• Vanem, Erik (2012). Bayesian hierarchical space-time models for the North Atlantic ocean wave climate.
• Vanem, Erik (2017). Dynamical Linear Models Applied to Ship Sensor Data. DNV GL. ISSN 978-82-515-0320-4.

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