Frequency band selection based on the kurtosis of the squared envelope spectrum and its application in bearing fault diagnosis

Selection of a suitable frequency band for envelope analysis is a key step towards successful diagnosis of rolling element bearing faults. The band selection requirements include: (a) effectively avoiding the frequency range which contains irrelevant periodic impulses, and (b) keeping the feature of impulsiveness for bearing signals. Popular frequency band selection approaches often aim at searching for the band which maximizes the envelope kurtosis or the kurtosis of the envelope spectrum, and thus may not satisfy those requirements simultaneously when weak bearing signals are masked by strong noisy impulses. To resolve this issue, the energy of envelope signal and its energy distribution in the spectrum were taken into consideration in the proposed approach. It was shown that, for weak impulsive signals, the kurtosis of squared envelope spectrum (KSES) decreases as the bandwidth increases provided that the initial band is wide enough to contain most of the signal energy. The decreasing trend of KSES was then used to determine bandwidths for predefined center frequencies. From the obtained narrow bands, the one with a large bandwidth and a low KSES was selected for further analysis by comparing the value of a quality index. The bandwidth determination method and the final band selection criterion effectively excluded irrelevant periodic impulses in the selected band and ensured the high impulsiveness of the extracted signal. The proposed method was validated using experimental data obtained from a bearing rig and a planetary gearbox test rig, and its advantages were highlighted by comparing with fast kurtogram and protrugram.

[1]  Bernard C. Picinbono,et al.  On circularity , 1994, IEEE Trans. Signal Process..

[2]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[3]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[4]  J. Antoni The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[5]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[6]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[7]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[8]  Robert B. Randall,et al.  Gear diagnostics in a planetary gearbox: a study using internal and external vibration signals , 2013 .

[9]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[10]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[11]  Robert B. Randall,et al.  Differential Diagnosis of Gear and Bearing Faults , 2002 .

[12]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[13]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[14]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[15]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[16]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[17]  Fabien Millioz,et al.  Circularity of the STFT and Spectral Kurtosis for Time-Frequency Segmentation in Gaussian Environment , 2011, IEEE Transactions on Signal Processing.

[18]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[19]  Robert B. Randall,et al.  Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size , 2011 .

[20]  Zhiqi Fan,et al.  A hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor , 2015 .