Browsing by Author "Devaney, Michael J."

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Citation - WoS: 3
Citation - Scopus: 4
Motor Condition Monitoring by Empirical Wavelet Transform
(IEEE Computer Soc, 2018) Eren, Levent; Cekic, Yalcin; Devaney, Michael J.
Bearing faults are by far the biggest single source of motor failures. Both fast Fourier (frequency based) and wavelet (time-scale based) transforms are used commonly in analyzing raw vibration or current data to detect bearing faults. A hybrid method, Empirical Wavelet Transform (EWT), is used in this study to provide better accuracy in detecting faults from bearing vibration data. In the proposed method, the raw vibration data is processed by fast Fourier transform. Then, the Fourier spectrum of the vibration signal is divided into segments adaptively with each segment containing part of the frequency band. Next, the wavelet transform is applied to all segments. Finally, inverse Fourier transform is utilized to obtain time domain signal with the frequency band of interest from EWT coefficients to detect bearing faults. The bearing fault related segments are identified by comparing rms values of healthy bearing vibration signal segments with the same segments of faulty bearing. The main advantage of the proposed method is the possibility of extracting the segments of interest from the original vibration data for determining both fault type and severity.
Citation - WoS: 9
Citation - Scopus: 11
Motor Current Signature Analysis Via Four-Channel Fir Filter Banks
(Elsevier Sci Ltd, 2016) Eren, Levent; Askar, Murat; Devaney, Michael J.
Motor current signature analysis (MCSA) is capable of providing continuous monitoring of induction motors in a non-intrusive manner. Fourier based techniques have been used widely in processing of stator current but these techniques have a shortcoming in processing non-stationary signals such as the stator current. Recently, wavelet packet decomposition (WPD) has become popular in such applications since it gives better results in the case of non-stationary signals. The latter approach has much higher computational complexity limiting its use in motor diagnostics applications. In this study, the use of four-channel FIR filter banks is proposed to provide lower computational complexity. Four-channel filter banks employ higher level of parallel processing than currently used two-channel filter banks. FPGA implementation of the proposed algorithm would result in even further reduction in overall computation time. (C) 2016 Elsevier Ltd. All rights reserved.