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Subject: search.filters.subject.Adaptive thresholds

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    A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection
    (Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Bakdi, Azzeddine
    Principal Component Analysis (PCA) is the most common Multivariate Statistical Process Control (MSPC) method that is widely used for Fault Detection and Diagnosis (FDD). Since early abnormality detection with high accuracy is required for safe and reliable process operation, False Alarms Rate (FAR), Missed Detection Rate (MDR) and the detection time delay are the major factors that must be taken into consideration when developing any process monitoring scheme. Unfortunately, the PCA performance, with fixed limits, is weak in terms of the stated factors. In contrast, conventional Moving Window PCA (MWPCA) is an adaptive technique which updates both the PCA model and the thresholds once a new normal observation is available. Yet, MWPCA methodology still does not reduce the MDR and the detection delay. In this paper, a Modified MWPCA (MMWPCA) with Fuzzy Logic Filter (FLF) is proposed to enhance the monitoring performance of PCA. It is an adaptive approach with a fixed model that combines both aforementioned techniques. The aim of using FLF is to ensure robustness to false alarms without affecting the Fault Detection (FD) performance. The application of the proposed method has been carried out on the Tennessee Eastman Process (TEP). Hold-one and hold-five MMWPCA with FLF are applied and compared to recent FDD work in the literature. The obtained results demonstrate the superiority of the proposed technique in detecting different types of faults with high accuracy and with shorter time delay
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    Fault detection in a grid-connected photovoltaic system using adaptive thresholding method
    (Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Halabi, Laith M.; Guichi, Amar; Mekhilef, Saad
    In this paper, an adaptive monitoring scheme with Fuzzy Logic Filter (FLF) is developed and applied to monitor a Grid-Connected Photovoltaic System (GCPVS). This method is based on Principal Component Analysis (PCA) and Moving Window Principal Component Analysis (MWPCA). It is designed to generate adaptive thresholds for its monitoring indices. The FLF filters the monitoring indices to reduce the number of False Alarms (FA) and increase the Fault Detection Rate (FDR). The application is carried out on the GCPVS of the Power Electronics and Renewable Energy Research Laboratory (PEARL) of Malaya University. The proposed technique is compared against PCA method in terms of FAR reduction. The detection ability of the adaptive thresholding with FLF monitoring scheme is tested first on simulated faults then it is applied to detect a real abnormal behaviour. The results show that the proposed method is effective in reducing the number of false alarms and in detecting different types of faults with high accuracy
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    A modified moving window dynamic PCA with fuzzy logic filter and application to fault detection
    (Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Bensmail, Abderazak
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    A new adaptive linear combined CFAR detector in presence of interfering targets
    (2011) Magaz, B.; Belouchrani, A.; Hamadouche, M.
    View at Publisher| Export | Download | More... Progress In Electromagnetics Research B Issue 34, 2011, Pages 367-387 A new adaptive linear combined CFAR detector in presence of interfering targets (Article) Magaz, B.a , Belouchrani, A.a, Hamadouche, M.b a Electronics Department, Ecole Nationale Polytechnique, Algiers, Algeria b Department of Physics, University of Boumerdes, Boumerdes, Algeria View references (15) Abstract In this paper, a new radar constant false alarm rate detector to perform adaptive threshold target detection in presence of interfering targets is proposed. The proposed CFAR detector, referred to as Adaptive Linear Combined CFAR, ALC-CFAR, employs an adaptive composite approach based on the well-known cell averaging CFAR, CA-CFAR, and the ordered statistics, OS-CFAR, detectors. Data in the reference window is used to compute an adaptive weighting factor employed in the fusion scheme. Based on this factor, the ALCCFAR tailors the background estimation algorithm. The conducted Monte Carlo simulation results demonstrate that the proposed detector provides low loss CFAR performance in an homogeneous environment and also performs robustly in presence of interfering targets. The performances of the ALC-CFAR detector have been evaluated and compared with that of the CA-CFAR and the OS-CFAR detectors. The obtained results are presented and discussed in this paper