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Subject: search.filters.subject.Fault detection

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    Interval-valued PCA-based approach for fault detection in dynamic systems
    (2022) Hellati, Sami Oussama; Kouari, Abdellah Anis; Kouadri, Abdelmalek (supervisor)
    Fault detection and diagnosis is an important domain in modern process engineering, where principal component analysis (PCA) is one of its powerful data-driven techniques. The use of PCA in dynamic systems will approximate the dynamic behavior with a static one, which is not convenient. To address this issue, one of the most well-known approaches is the use of time- lag-shifted data; this approach is known as dynamic principal component analysis (DPCA). However, DPCA is still not an optimal solution due to the effect of uncertainties on the model parameters, which will lead to drifts and affect the performance of the model. In this disser- tation, a new approach is proposed to overcome this issue by including uncertainties in the modeling phase, which will ensure a safe interval for the data to fluctuate. This approach is called interval-valued dynamic principal component analysis (IV-DPCA). To test the perfor- mance of IV-DPCA, real data obtained from a cement manufacturing plant were used to build and test the PCA, DPCA, and IV-DPCA models, then the three models were compared to each other in terms of false alarm rate (FAR), missed alarms rate (MDR), and detection time delay (DTD).
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    On fault detection in a grid-connected photovoltaic system under two different operating modes
    (2018) Harkane, Mohamed Amine; Kouadri, Abdelmalek (Supervisor); Bakdi, Azzeddine (Supervisor)
    This project proposes an efficient fault detection method based on process history method using Principal Component Analysis (PCA) technique in dimension reduction and feature extraction for Grid Connected PhotoVoltaic System (GCPV) labeled data. The Fault Indicator (FI) developed in this work is an elliptic threshold based on the gaussian distribution of Normal Operation Condition (NOC) of the two different operating modes, Maximum Power Point Tracking (MPPT) and Power Rating (PR), of the system. The proposed fault detection method has been validated experimentally by the evaluation of the False Alarms Rate (FAR), Fault Detection Rate (FDR) and the Detection Delay (DD).The results