Azzeddine Bakdi

Postdoctoral Fellow - Statistics and Data Science
Image of Azzeddine Bakdi
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Room 803
Visiting address Moltke Moes vei 35 Niels Henrik Abels hus 0851 OSLO
Postal address Postboks 1053 Blindern 0316 OSLO

My background studies cover the fields of Electrical and Control Engineering; And my recent PhD research focused on statistical analysis tools for fault detection and process monitoring.

As a member of the Sensor Systems group at BigInsight research centre, my research interests are to exploit statistical and machine learning tools and develop novel methods  to explore real-time sensors measurements for big data analysis and information extraction. The main objectives are to construct efficient, robust and reliable strategies for fault detection, diagnosis, and reconstruction in modern industrial systems and particularly the maritime sector.

Tags: Statistics and biostatistics, Statistics, Fault detection, Big data, Industrial systems


- A. Bakdi, A. Kouadri, S. Mekhilef (2019). "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones." Renewable & Sustainable Energy Reviews 103: 546- 555.

- A. Bakdi and A. Kouadri (2017). "An improved plant-wide fault detection scheme based on PCA and adaptive threshold for reliable process monitoring: Application on the new revised model of Tennessee Eastman process." Journal of Chemometrics,

- A. Hamadouche, A. Kouadri, A. Bakdi. (2017). "A modified Kullback divergence for direct fault detection in large scale systems." Journal of Process Control 59: 28-36.

- A. Bakdi, A. Kouadri, A. Bensmail. (2017). "Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme." Control Engineering Practice 66: 64-75.

- A. Bakdi, et al. (2017). "Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control." Robotics and Autonomous Systems 89: 95-109.

- A. Bakdi and A. Kouadri (2017). "A new adaptive PCA based thresholding scheme for fault detection in complex systems." Chemometrics and Intelligent Laboratory Systems 162: 83-93.

- M. Ammiche, A. Kouadri, A. Bakdi. (2018). "A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection." Chemical Engineering Science 187: 269-279.

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Published Oct. 2, 2018 1:56 PM - Last modified Sep. 2, 2019 2:11 PM

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