Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings.

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.

[1]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

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

[3]  Yanyang Zi,et al.  Repetitive transients extraction algorithm for detecting bearing faults , 2016, 1601.02339.

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

[5]  S. Mallat.  A wavelet tour of signal processing , 1998 .

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

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

[8]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

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

[11]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

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

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

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

[15]  Shuilong He,et al.  Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis , 2016 .

[16]  Yaguo Lei,et al.  Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .

[17]  R. Wiggins.  Minimum entropy deconvolution , 1978 .

[18]  Radoslaw Zimroz,et al.  Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings , 2018 .

[19]  Dongpu Cao,et al.  Advanced Power-Source Integration in Hybrid Electric Vehicles: Multicriteria Optimization Approach , 2015, IEEE Transactions on Industrial Electronics.

[20]  Xiaodong Jia,et al.  A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery , 2017 .

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

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

[23]  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 .

[24]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[25]  Zhenpo Wang,et al.  Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles , 2017, IEEE Transactions on Vehicular Technology.

[26]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[27]  Yonghao Miao,et al.  Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification , 2017 .

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

[29]  Zhaohui Du,et al.  Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis , 2017 .

[30]  Qing Zhao,et al.  Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection , 2017 .

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

[32]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

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

[34]  Robert B. Randall,et al.  Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .

[35]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[36]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

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

[38]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[39]  Ruqiang Yan,et al.  Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis , 2016 .

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

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

[42]  Xuejun Li,et al.  Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis , 2014, Sensors.

[43]  Mattia Zorzi,et al.  Multivariate Spectral Estimation Based on the Concept of Optimal Prediction , 2014, IEEE Transactions on Automatic Control.

[44]  Michele Pavon,et al.  A Maximum Entropy Enhancement for a Family of High-Resolution Spectral Estimators , 2012, IEEE Transactions on Automatic Control.

[45]  Yanyang Zi,et al.  Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet , 2013 .

[46]  Mattia Zorzi,et al.  A New Family of High-Resolution Multivariate Spectral Estimators , 2012, IEEE Transactions on Automatic Control.

[47]  Qiang Miao,et al.  Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings , 2018, Mechanical Systems and Signal Processing.

[48]  Wei He,et al.  Bearing fault detection based on optimal wavelet filter and sparse code shrinkage , 2009 .

[49]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

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

[51]  Kang Zhang,et al.  An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems , 2012 .

[52]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..