Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper. In the proposed STEWT-PGMCKD method, the scale space threshold method is designed to solve the problems of falling into local extremum and mode over decomposition caused by the local-max-min band decomposition method of EWT, which is used to decompose the frequency band of signal, and the correlation analysis is carried out between the decomposed modal components and the original signal to retain the modal components with high correlation. Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size $L$ and deconvolution period $T$ . Nextly, the parameters of the MCKD are optimized by grid search method. Finally, the power spectrum analysis of the enhanced signal is carried out to realize the feature extraction and fault diagnosis. The experiment results show that the proposed STEWT-PGMCKD method can effectively extract the weak fault information and accurately realize the fault diagnosis for rolling bearings.

[1]  Yongquan Zhou,et al.  An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application , 2021, Expert Syst. Appl..

[2]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[3]  Te Han,et al.  Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification , 2018 .

[4]  Shuilong He,et al.  Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform , 2020 .

[5]  Ming Zhao,et al.  Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery , 2021 .

[6]  Xiang Li,et al.  Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places , 2020, IEEE Transactions on Industrial Electronics.

[7]  Jun Zhang,et al.  Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution , 2020 .

[8]  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 .

[9]  Min Xie,et al.  A Real-Time Fault Diagnosis Methodology of Complex Systems Using Object-Oriented Bayesian Networks , 2016, Bayesian Networks in Fault Diagnosis.

[10]  Jianshe Kang,et al.  Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution , 2015 .

[11]  Yaguo Lei,et al.  Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings , 2017 .

[12]  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 .

[13]  Jinde Zheng,et al.  Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method , 2019, IEEE Access.

[14]  Qiang Wang,et al.  Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis , 2019, Measurement.

[15]  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.

[16]  Rui Yao,et al.  A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm , 2017, Soft Computing.

[17]  Kefu Liu,et al.  Condition assessment of structure with tuned mass damper using empirical wavelet transform , 2018 .

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

[19]  Xiaolong Wang,et al.  Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution , 2016 .

[20]  Hong Jiang,et al.  Comparative Study on Dynamic Characteristics of Two-Stage Gear System With Gear and Shaft Cracks Considering the Shaft Flexibility , 2020, IEEE Access.

[21]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[22]  Xiangbing Zhou,et al.  Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models , 2021, Complex..

[23]  Jiawei Xiang,et al.  A time–frequency-based maximum correlated kurtosis deconvolution approach for detecting bearing faults under variable speed conditions , 2019, Measurement Science and Technology.

[24]  Wu Deng,et al.  Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System , 2019, IEEE Access.

[25]  Wu Deng,et al.  Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings , 2021, IEEE Transactions on Instrumentation and Measurement.

[26]  Qing Zhao,et al.  Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .

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

[28]  Hossam Faris,et al.  Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..

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

[30]  Xiaobo Liu,et al.  Compound faults diagnosis based on customized balanced multiwavelets and adaptive maximum correlated kurtosis deconvolution , 2019, Measurement.

[31]  Xiao-Zhi Gao,et al.  MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization , 2021 .

[32]  Yu Yang,et al.  Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing , 2020, Knowl. Based Syst..

[33]  Wenhua Du,et al.  Research and application of improved adaptive MOMEDA fault diagnosis method , 2019, Measurement.

[34]  Chao Liu,et al.  Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.

[35]  Chunhui Zhao,et al.  Fault Diagnosis With Dual Cointegration Analysis of Common and Specific Nonstationary Fault Variations , 2020, IEEE Transactions on Automation Science and Engineering.

[36]  Yi Wang,et al.  M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems , 2019 .

[37]  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..

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

[39]  Wu Deng,et al.  Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem , 2020, Appl. Soft Comput..

[40]  Shunming Li,et al.  A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions , 2018, IEEE Access.

[41]  Huiling Chen,et al.  Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis , 2020, Appl. Soft Comput..

[42]  Min Xie,et al.  A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults , 2017, IEEE Transactions on Automation Science and Engineering.

[43]  David He,et al.  A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network , 2019, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability.

[44]  Chao Liu,et al.  Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems , 2018, Knowl. Based Syst..

[45]  Zhiwei Wang,et al.  Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis. , 2019, ISA transactions.

[46]  Haifeng Wang,et al.  Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study , 2020, IEEE Transactions on Industrial Electronics.

[47]  Chao Liu,et al.  Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. , 2019, ISA transactions.

[48]  W. Sweldens.  The Lifting Scheme: A Custom - Design Construction of Biorthogonal Wavelets "Industrial Mathematics , 1996 .

[49]  Chenxi Liu,et al.  Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn , 2018 .

[50]  Haodong Liu,et al.  Performance Prediction Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy and Extreme Learning Machine , 2020, IEEE Transactions on Instrumentation and Measurement.

[51]  Pradip Sircar,et al.  A novel approach for automated detection of focal EEG signals using empirical wavelet transform , 2016, Neural Computing and Applications.

[52]  Chao Liu,et al.  An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems , 2019, Mechanical Systems and Signal Processing.

[53]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .