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Wahiba Bounoua

Doctoral Research Fellow

Department of Engineering Sciences

Email: wahiba.bounoua@uia.no
Phone: +47 37233453

Office D3064 (Jon Lilletuns vei 9, 4879 Grimstad, Norway)

Areas of responsibility

Wahiba Bounoua is a Ph.D. research fellow in mechatronics at the University of Agder, Norway. She obtained a bachelor’s degree in Electrical and Electronics Engineering and a master's degree in Control Engineering from the Institute of Electrical and Electronics Engineering, Algeria. She has extensive interests in statistical signal processing and machine learning techniques developed for industrial applications, that foster safety, availability and reliability.

Publications

Bounoua, W., Aftab, M. F., & Omlin, C. W. P. (2023). Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks. IFAC-PapersOnLine. ISSN: 2405-8963. 56 (2). s 11687 - 11692. doi:10.1016/j.ifacol.2023.10.523.

Bounoua, W., Aftab, M. F., & Omlin, C. W. P. (2023). "Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops". Computers & Chemical Engineering, 108173. https://doi.org/10.1016/j.compchemeng.2023.108173

Bounoua, W., Aftab, M. F., & Omlin, C. W. P. (2022). "Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis". Industrial & Engineering Chemistry Research, acs.iecr.2c02785. https://doi.org/10.1021/acs.iecr.2c02785

Research

Selected publications

Bounoua, W., & Bakdi, A. (2021). Fault detection and diagnosis of nonlinear dynamical processes through correlation dimension and fractal analysis based dynamic kernel PCA. Chemical Engineering Science, 229, 116099. https://doi.org/10.1016/j.ces.2020.116099
Bakdi, A., Bounoua, W., Guichi, A., & Mekhilef, S. (2021). Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence. International Journal of Electrical Power & Energy Systems, 125, 106457. https://doi.org/10.1016/j.ijepes.2020.106457
Bounoua, W., Benkara, A. B., Kouadri, A., & Bakdi, A. (2020). Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection. Transactions of the Institute of Measurement and Control, 42(6), 1225–1238. https://doi.org/10.1177/0142331219888370
Bakdi, A., Bounoua, W., Mekhilef, S., & Halabi, L. M. (2019). Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV. Energy, 189, 116366. https://doi.org/10.1016/j.energy.2019.116366

Publications

Scientific articles and book chapters

Bounoua, Wahiba & Aftab, Muhammad Faisal (2024). Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops. Journal of Process Control. ISSN 0959-1524. 138. doi: 10.1016/j.jprocont.2024.103226.
Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2023). Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks. IFAC-PapersOnLine. ISSN 2405-8963. 56(2), p. 11687–11692. doi: 10.1016/j.ifacol.2023.10.523.
Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2023). Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops. Computers and Chemical Engineering. ISSN 0098-1354. 172. doi: 10.1016/j.compchemeng.2023.108173. Show summary
Detrended Fluctuation Analysis (DFA) is a reliable and assumption-free approach for gauging the complexity of a time series. In this paper, an online oscillations detection paradigm is presented, which integrates the potential of DFA in detecting abnormal coherent fluctuations with the Empirical Wavelet Transform (EWT) efficiency in extracting the characteristics of oscillations. However, the standard EWT fails to separate modes oscillating at close frequencies, resulting in an incorrect decomposition. Furthermore, the lack of an appropriate stopping criterion frequently results in the signal being over-decomposed into several inconsequential components. Therefore, owing to the capability of DFA to differentiate between fluctuations stemming from noise and coherent fluctuations arising from genuine oscillations, an Improved EWT (IEWT) is presented to mitigate these issues and accurately extract only compelling oscillating modes. The proposed DFA-based IEWT framework is verified on simulated applications and data from real industrial processes, illustrating its effectiveness.
Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2022). Controller performance monitoring: A survey of problems and a review of approaches from a data-driven perspective with a focus on oscillations detection and diagnosis. Industrial & Engineering Chemistry Research. ISSN 0888-5885. 61(49), p. 17735–17765. doi: 10.1021/acs.iecr.2c02785. Show summary
Optimal operations of industrial control systems require rigorous monitoring to ensure safety, increase profitability, and minimize plant maintenance downtime. Thus, controller performance monitoring has been actively pursued by the research community, resulting in increased research publications over the past two decades. The availability of large data sets, the so-called big data era, has led impetus to the data-driven domain of controller performance monitoring. In this paper, a comprehensive review of the data-driven research in the CPM domain is presented. To illustrate the rationale behind the research efforts, a succinct explanation of the faults observed in control loops and their influence on overall controller performance is also presented. The paper then provides the publicly available data repositories adopted by most researchers to assess and compare their performance monitoring techniques realistically. Moreover, a review of the most eminent techniques proposed so far concerning both detection and diagnosis phases is presented.

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Published Apr. 16, 2024 11:24 AM