Multisensor fault detection and isolation using Kullback Leibler Divergence : Application to data vibration signals

Abstract : In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Principal Component Analysis (PCA) method is used to extract the features and to reduce the data dimension. Then, the Kullback-Leibler Divergence (KLD) is used to detect the fault occurrence by comparing the Probability Density Function of the latent scores. The faulty sensor is isolated thanks to a linear combination of the original measurements with binary coefficients denoted as Z-decomposition. The proposed approach is experimentally verified with vibration signals used for monitoring bearings in electrical machines.
Type de document :
Communication dans un congrès
IEEE. International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Aug 2017, Shanghai, China. 〈10.1109/sdpc.2017.65 〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01577713
Contributeur : Claude Delpha <>
Soumis le : dimanche 27 août 2017 - 23:10:00
Dernière modification le : jeudi 5 avril 2018 - 12:30:24

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Claude Delpha, Demba Diallo, Wang Tianzhen, Jie Liu, Zelig Li. Multisensor fault detection and isolation using Kullback Leibler Divergence : Application to data vibration signals. IEEE. International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Aug 2017, Shanghai, China. 〈10.1109/sdpc.2017.65 〉. 〈hal-01577713〉

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