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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperHas files: NoSubject: search.filters.subject.Feature ExtractionSubject: search.filters.subject.Feature SelectionAuthor: search.filters.author.Cekic, Yalcin

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    Enhanced feature selection from wavelet packet coefficients in fault diagnosis of induction motors with artificial neural networks
    (2010) Eren, Levent; Cekic, Yalcin; Devaney, Michael Joseph; Eren, Levent, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United States
    Wavelet packet decomposition (WPD) of line current has been successfully applied in motor fault detection. Enhanced feature selection from wavelet packet coefficients (WPCs) is presented in this paper. This method involves the decomposition of motor current into equally spaced frequency bands by using an all-pass implementation of elliptic IIR half-band filters in the filter bank structure to obtain WPCs in a computationally efficient way. Then, the bias in WPCs for each frequency band is removed to suppress both power system harmonics and leakage from adjacent frequency bands. Finally, the enhanced features are used as inputs to an ANN to provide motor fault detection with higher fault detection rate. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.