Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Subject: search.filters.subject.Adaptive threshold

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    Sensor fault detection, localization, and system reconfiguration with a sliding mode observer and adaptive threshold of PMSM
    (2016) Aibeche, Abderrazak; Kidouche, Madjid
    This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of “abc” currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm’s performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board
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    Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme
    (Elsevier, 2017) Bakdi, Azzeddine; Kouadri, Abdelmalek; Bensmail, Abderazak
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    A new adaptive PCA based thresholding scheme for fault detection in complex systems
    (Elsevier, 2017) Bakdi, Azzeddine; Kouadri, Abdelmalek
    For large scale and complex processes, data-driven analysis methods are receiving increasing attention for fault detection and diagnosis to improve process operation by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. Common methods based on multivariate statistical analysis are widely used and particularly principal component analysis (PCA), fault detection indices used along with PCA including the Hotelling T² statistic and the sum of squared prediction error (SPE) known as the Q statistic can be used to identify faults. This paper develops a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic, which is effective in detecting small changes and abrupt shifts in the process operation. The aim is to enhance the performance of PCA methods for process monitoring, while maintaining a low false alarm rate with good sensitivity of anomalies. The performance of the developed scheme is compared to a conventional fixed thresholding technique by evaluating the detection performance across various types of faults that occurred in the Tennessee Eastman Process, The results demonstrate the promising capabilities of our proposed scheme
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    An adaptive threshold estimation scheme for abrupt changes detection algorithm in a cement rotary kiln
    (Elsevier, 2014) Kouadri, Abdelmalek; Bensmail, Abderazak; Kheldoun, Aissa; Refoufi, Larbi