Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation

Abstract Bearing incipient fault feature extraction is crucial and challenging throughout its life cycle. In this paper, an adaptive period matching enhanced sparse representation (APMESR) algorithm is developed to address this issue. First, a novel methodology for estimating the period of faulty impulses is proposed from the perspective of mining the periodicity-related numerical patterns. Second, the period estimation methodology is embedded in a sparse representation model to implement adaptive period matching to form APMESR, which is capable of achieving periodic sparsity. Third, maximal overlap discrete wavelet packet transform is selected as the linear transformation of APMESR for improving its ability to reduce noise and highlight periodic impulse signatures. Furthermore, evaluations and comparisons are conducted using simulations to demonstrate the validity and performance of the proposed period estimation methodology, linear transformation, and APMESR. Experimental results indicate that APMESR can effectively extract incipient bearing fault features and outperforms other well-advanced methods.

[1]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[2]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[3]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[4]  Pietro Borghesani,et al.  A statistical methodology for the design of condition indicators , 2019, Mechanical Systems and Signal Processing.

[5]  Fan Jiang,et al.  An Improved VMD With Empirical Mode Decomposition and Its Application in Incipient Fault Detection of Rolling Bearing , 2018, IEEE Access.

[6]  Xiaodong Wang,et al.  Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator. , 2018, ISA transactions.

[7]  Ivan W. Selesnick,et al.  L1-norm penalized least squares with SALSA , 2014 .

[8]  Bo Peng,et al.  The FERgram: A rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio , 2019, Journal of Mechanical Science and Technology.

[9]  Y. Lei,et al.  An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings , 2017 .

[10]  Marc Thomas,et al.  A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection , 2017 .

[11]  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 .

[12]  Zhibin Zhao,et al.  Hierarchical hyper-Laplacian prior for weak fault feature enhancement. , 2019, ISA transactions.

[13]  Zhixiong Li,et al.  Incipient rolling element bearing weak fault feature extraction based on adaptive second-order stochastic resonance incorporated by mode decomposition , 2019, Measurement.

[14]  Hongkai Jiang,et al.  Periodicity-enhanced sparse representation for rolling bearing incipient fault detection. , 2021, ISA transactions.

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

[16]  Rick Chartrand,et al.  Compressed sensing recovery via nonconvex shrinkage penalties , 2015, ArXiv.

[17]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[18]  Ruixin Wang,et al.  An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm , 2020 .

[19]  Jianhui Lin,et al.  Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection , 2018 .

[20]  Xuefeng Chen,et al.  Reweighted generalized minimax-concave sparse regularization and application in machinery fault diagnosis. , 2020, ISA transactions.

[21]  Yongbo Li,et al.  Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.

[22]  Ivan W. Selesnick,et al.  Resonance-based signal decomposition: A new sparsity-enabled signal analysis method , 2011, Signal Process..

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

[24]  A. Walden,et al.  The phase–corrected undecimated discrete wavelet packet transform and its application to interpreting the timing of events , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[26]  Ming J. Zuo,et al.  Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples , 2017 .

[27]  Gaigai Cai,et al.  Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis , 2018, Journal of Sound and Vibration.

[28]  Shaowei Liu,et al.  Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis , 2022 .

[29]  Xuefeng Chen,et al.  Sparsity-Assisted Fault Feature Enhancement: Algorithm-Aware Versus Model-Aware , 2020, IEEE Transactions on Instrumentation and Measurement.

[30]  Dewei Yang,et al.  A group sparse representation method in frequency domain with adaptive parameters optimization of detecting incipient rolling bearing fault , 2019 .

[31]  Ivan W. Selesnick,et al.  Sparse Regularization via Convex Analysis , 2017, IEEE Transactions on Signal Processing.

[32]  Yaguo Lei,et al.  A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings , 2020, IEEE Transactions on Reliability.

[33]  Yahui Cui,et al.  Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis , 2018, Shock and Vibration.

[34]  Zhibin Zhao,et al.  Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis , 2019, IEEE Transactions on Industrial Electronics.

[35]  Xiao Yu,et al.  Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection , 2018 .

[36]  Gaigai Cai,et al.  Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[37]  Yi Zhang,et al.  Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD. , 2019, ISA transactions.

[38]  Mohammad Karimi,et al.  Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems. , 2017, ISA transactions.

[39]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

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

[41]  Robert D. Nowak,et al.  Majorization–Minimization Algorithms for Wavelet-Based Image Restoration , 2007, IEEE Transactions on Image Processing.

[42]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .