A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis

Rolling bearing plays an important role in the overall operation of the mechanical system; therefore, it is necessary to monitor and diagnose the bearings. Kurtosis is an important index to measure impulses. Fast Kurtogram method can be applied to the fault diagnosis of rolling bearings by extracting maximum kurtosis component. However, the final result may disperse the effective fault information to different frequency bands or find wrong frequency band, resulting in inaccurate frequency band selection or misdiagnosis. In order to find the maximum component of kurtosis accurately, an algorithm of frequency band multidivisional and overlapped based on EWT (MDO-EWT) is proposed in this paper. This algorithm changes the traditional Fast Kurtogram frequency bands division method and filtering method. It builds the EWT boundaries based on the maximum kurtosis component in each iteration and finally obtains the maximum kurtosis component. Through the simulation signal and the rolling bearing inner and outer ring fault signals verification, it is proved that the proposed method has a good performance on accuracy and effectiveness.

[1]  Lingli Cui,et al.  Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis , 2017, IEEE Transactions on Industrial Electronics.

[2]  Kun Zhang,et al.  Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis , 2019, Measurement Science and Technology.

[3]  Kathryn Heal,et al.  A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation , 2014, Int. J. Wavelets Multiresolution Inf. Process..

[4]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[5]  Jean-Louis Lacoume,et al.  Blind separation of wide-band sources: Application to rotating machine signals , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

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

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

[8]  F. Combet,et al.  Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .

[9]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[10]  Diego Cabrera,et al.  Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram , 2016 .

[11]  Radoslaw Zimroz,et al.  Selection of informative frequency band in local damage detection in rotating machinery , 2014 .

[12]  V. Sowmya,et al.  Empirical Wavelet Transform for Multifocus Image Fusion , 2016 .

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

[14]  Ming Zhao,et al.  Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis , 2016 .

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

[16]  Zi Yanyang.  Spectral Kurtosis of Multiwavelet for Fault Diagnosis of Rolling Bearing , 2010 .

[17]  Antoine Tahan,et al.  A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis , 2016 .

[18]  Jianshe Kang,et al.  A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis , 2015 .

[19]  R. Dwyer.  Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals , 1984 .

[20]  Kun Zhang,et al.  An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis , 2018, Applied Sciences.

[21]  Tian Fu-qin.  Improved Harmonic Wavelet Packet Kurtogram and Its Application , 2014 .