Wahiba Bounoua

• 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), s. 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. Vis sammendrag

  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), s. 17735–17765. doi: 10.1021/acs.iecr.2c02785. Vis sammendrag

  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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