Publications Scientifiques
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Item False alarms rate reduction using filtered monitoring indices(UMBB, 2017) Ammiche, Mustapha; Kouadri, A.False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussedItem Constant false alarms rate for fault detection(IEEE, 2017) Ammiche, Mustapha; Kouadri, AbdelmalekFault detection needs to be accurate and precise to make right decisions about the systems operation status, unfortunately, monitoring processes via multivariate statistical control (MSPC) such as principal component analysis (PCA) arises the problem of false alarms. One solution to this problem is to increase the confidence intervals of the monitoring indices thresholds; however, doing that will decrease the sensitivity of PCA, thus, the detection ability of this technique is risked to be lost. In this paper, a Constant False Alarms Rate (CFAR) is proposed to decrease the number false alarms by investigating on the number of false alarms. Therefore, the fault detection accuracy will be increased. The advantage of using CFAR is that it does not need to change the confidence intervals. The proposed method has been tested on real data of cement rotary kiln to evaluate its capability detection of abrupt, intermittent and ramp changes. The obtained results demonstrate that the developed technique is able to detect different types of faults with no false alarms; furthermore, a real fault has been successfully detectedItem A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection(Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Bakdi, AzzeddinePrincipal 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 delayItem Fault detection in a grid-connected photovoltaic system using adaptive thresholding method(Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Halabi, Laith M.; Guichi, Amar; Mekhilef, SaadIn 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 accuracyItem A modified moving window dynamic PCA with fuzzy logic filter and application to fault detection(Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Bensmail, Abderazak