Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator. (1st February 2018)

• Record Type: Journal Article
• Authors: Singh, Jaskaran; Darpe, A.K.; Singh, S.P.
• Is Part Of: Mechanical systems and signal processing. Volume 100(2018)
• Page Start: 662
• Page End: 693
• Publication Date: 2018-02-01
• Subjects: Kurtogram -- Over-complete rational dilation wavelet transform -- Rolling element bearing -- Fault diagnosis -- Autocorrelation; AC autocorrelation coefficient -- BDF ball defect frequency -- HFRT high frequency resonance technique -- EO energy operator -- FFT fast Fourier transform -- FK fast kurtogram -- IRDF inner race defect frequency -- MRFB multi rate filter bank -- ORDF outer race defect frequency -- ORDWT over complete rational dilation wavelet transform -- SNR Signal to noise ratio -- SK spectral kurtosis -- STFT short time Fourier transform -- TEO-AK temporal energy operated auto correlated kurtosis -- WK wavelet kurtogram -- WPD wavelet packet decomposition; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.06.040
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A bearing fault diagnosis approach based on ORDWT is presented. Proposed a robust indicator 'TEO-AK' for evaluating signal impulsiveness. TEO-AK measure is suggested to be sensitive to fault severity. Autocorrelation of optimal sub band's energy time series is proposed to classify faults. Performance is validated on simulated and experimental test vibration data. Abstract: Local damage in rolling element bearings usually generates periodic impulses in vibration signals. The severity, repetition frequency and the fault excited resonance zone by these impulses are the key indicators for diagnosing bearing faults. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing fault excited resonance zone is based on the maximization of a proposed impulse detection measure "Temporal energy operated auto correlated kurtosis". The proposed indicator is robust and consistent in evaluating the impulsiveness of fault signals in presence of interfering vibration such as heavy background noise or sporadic shocks unrelated to the fault or normal operation. The structure of the proposed indicator enables it to be sensitive to fault severity. For enhanced fault classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.