A Novel Rolling Bearing Fault Diagnosis Method Based on Empirical Wavelet Transform and Spectral Trend

Empirical wavelet transform (EWT) is a new adaptive signal decomposition method based on wavelet theory, the main idea is to establish an appropriate set of empirical wavelet filter banks for adaptive signal decomposition. EWT has been demonstrated its effectiveness in some applications. However, the unreasonable spectrum segmentation will lead to the emergence of many invalid components. In this paper, a novel spectral segmentation method is proposed to improve the drawback of EWT in boundary division. The proposed method takes into account the waveform of the spectrum itself. First, different spectral trends are obtained by iteratively calculating the mean of the upper envelope function and the lower envelope function of the spectrum. Then, the most appropriate one is got according to the criterion and the spectrum segmentation is achieved by detecting the local minimum of the trend. Finally, empirical modes are obtained by a set of bandpass filters. The effectiveness and efficiency of the method are verified by two simulation signals. Finally, the proposed method is applied to fault diagnosis of the inner and outer race of rolling bearings, respectively. The results indicate that the method can accurately and effectively identify fault information.

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

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

[3]  Joshua R. Smith,et al.  The local mean decomposition and its application to EEG perception data , 2005, Journal of The Royal Society Interface.

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

[5]  Hongguang Li,et al.  An enhanced empirical wavelet transform for noisy and non-stationary signal processing , 2017, Digit. Signal Process..

[6]  Norden E. Huang,et al.  On Hilbert Spectral Representation: a True Time-Frequency Representation for nonlinear and nonstationary Data , 2011, Adv. Data Sci. Adapt. Anal..

[7]  Yi Yang,et al.  A rotating machinery fault diagnosis method based on local mean decomposition , 2012, Digit. Signal Process..

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

[9]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[10]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Huaqing Wang,et al.  A Novel Feature Enhancement Method Based on Improved Constraint Model of Online Dictionary Learning , 2019, IEEE Access.

[12]  Fangfang Zhang,et al.  A Novel Fault Diagnosis Method of Rolling Bearings Based on AFEWT-KDEMI , 2018, Entropy.

[13]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[14]  Zhaoheng Liu,et al.  A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis , 2016 .

[15]  Shaoze Yan,et al.  Revised empirical wavelet transform based on auto-regressive power spectrum and its application to the mode decomposition of deployable structure , 2018, Journal of Sound and Vibration.

[16]  Jinglong Chen,et al.  Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment , 2016 .

[17]  Hongguang Li,et al.  Succinct and fast empirical mode decomposition , 2017 .

[18]  Shaoze Yan,et al.  A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform , 2018 .

[19]  Jie Wang,et al.  Fault Diagnosis of Rolling Bearings Based on EWT and KDEC , 2017, Entropy.

[20]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[21]  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.

[22]  Ram Bilas Pachori,et al.  Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals , 2018, Digit. Signal Process..

[23]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[24]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[25]  Jianjun Zhou,et al.  Fast Empirical Mode Decomposition Based on Gaussian Noises , 2016, 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI).

[26]  Haiyang Pan,et al.  Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis , 2017, Signal Process..

[27]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[28]  Hojjat Adeli,et al.  A new music-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals , 2015, Digit. Signal Process..

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

[30]  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..

[31]  Zhixiong Li,et al.  A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator , 2016 .

[32]  Cai Yi,et al.  Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings , 2018 .

[33]  Shi Li,et al.  A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals , 2019, Comput. Ind..

[34]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

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