Browsing by Author "Cekic, Yalcin"

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Citation - WoS: 10
Citation - Scopus: 11
Broken rotor bar detection via four-band wavelet packet decomposition of motor current
(Springer, 2018) Cekic, Yalcin; Eren, Levent
The induction motor current is nonstationary by nature, and time-scale analysis techniques such as wavelet packet decomposition (WPD) are more suitable for the analysis of the stator current for broken rotor bar detection. But, WPD is very costly in terms of the computational effort when half-band FIR filter banks are used in analysis. The implementation of four-band FIR filter banks in the analysis of a phase current is proposed here to reduce the computational cost. The use of four-band FIR filter banks with FPGA implementation would also provide higher levels of parallel processing capability resulting in further reduction in computational time required for detection of broken rotor bar faults. In WPD, it is also possible to tailor the frequency band size (resolution) so that one frequency band covers all motor fault induced frequencies due to rotor speed variations. Here, the rms value for broken rotor bar fault-related frequency band is compared with the baseline data to detect any broken rotor bar faults.
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.