Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis

The fast kurtogram (FK), as a fast and effective method for fault diagnosis, is well accepted by many experts and scholars. However, the FK can only estimate the bandwidth and central frequency which come from resonance modulation of the signal. Sometimes useful information (containing faults) may be lost due to the inaccuracy of the estimated center frequency or bandwidth. In this paper, a novel method named empirical scanning spectrum kurtosis (ESSK), based on empirical wavelet transform (EWT), is proposed. Constructed by the principle of EWT, a set of filters with varying bandwidth scan and filter the whole frequency domain from low to high and a series of empirical modal components are obtained. Then, the spectral kurtosis (SK) of these components is calculated. The center frequency and bandwidth corresponding to the component which has the maximum SK are selected as the optimal center frequency and bandwidth. This method can adaptively and accurately find the frequency band containing rich fault feature information, and extract the corresponding component. Multiple simulation signals and experimental signals are used to verify the effectiveness of the proposed method. The results show that the method can maximally extract the components which contain the periodic pulse information and accurately diagnose the faults of the rolling bearing. In addition, comparisons with three popular signal processing methods, including the sparsogram, fir-based FK and short-time Fourier transform (STFT)-based FK are conducted to highlight the superiority of the proposed method.

[1]  Fulei Chu,et al.  HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault , 2019, Mechanical Systems and Signal Processing.

[2]  Hong Jiang,et al.  A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing , 2019, Measurement.

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

[4]  Alessandro Fasana,et al.  The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis , 2018 .

[5]  Baoxiang Wang,et al.  Extraction of repetitive transients with frequency domain multipoint kurtosis for bearing fault diagnosis , 2018 .

[6]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[7]  Xiaohui Gu,et al.  Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis , 2016 .

[8]  Robert B. Randall,et al.  Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference , 2016 .

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

[10]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

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

[12]  Jing Wang,et al.  Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[13]  Weiguo Huang,et al.  Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection , 2014, Signal Process..

[14]  Peter W. Tse,et al.  The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2” , 2013 .

[15]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[16]  Peter W. Tse,et al.  An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .

[17]  Jing Yuan,et al.  Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications for rotating machinery fault detection , 2012 .

[18]  Ming Liang,et al.  Identification of multiple transient faults based on the adaptive spectral kurtosis method , 2012 .

[19]  Ming Liang,et al.  An adaptive SK technique and its application for fault detection of rolling element bearings , 2011 .

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

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

[22]  Wensheng Su,et al.  Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement , 2010 .

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

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

[25]  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 .

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

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

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

[29]  Giorgio Tacconi,et al.  Filtering of randomly occurring signals by kurtosis in the frequency domain , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[30]  R. Dwyer.  A technique for improving detection and estimation of signals contaminated by under ice noise , 1982 .