Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing

Abstract Aiming at overcoming the limitations of permutation entropy (PE) in dynamic bearing health monitoring (Healthy-initial fault inception-severe fault stage) , in this paper, PE calculation based on tunable Q factor wavelet transform (TQWT) is implemented to quantify bearing health. Firstly, weaknesses of PE in depicting bearing health are studied through numerically simulated signals. Secondly, based on the biasness of PE towards bearing fundamental signal component (FSC), oscillation based method TQWT is used to separate the FSC from the bearing signal to calculate its PE. Since, the proposed PE calculation method includes an oscillation based procedure, the resultant feature value is termed as oscillation based PE (OBPE). Selection of the parameters needed for OBPE calculation are discussed in details. Two public bearing run to failure experiments have confirmed that OBPE provides a much better result than direct application of PE in dynamic bearing health monitoring.

[1]  Cong Wang,et al.  Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform , 2019, Measurement.

[2]  Yongbo Li,et al.  A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree , 2016 .

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

[4]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .

[5]  Tian Ran Lin,et al.  An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis , 2019, Measurement.

[6]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[7]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[8]  C. Bandt.  Ordinal time series analysis , 2005 .

[9]  Ming Liang,et al.  A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform , 2013 .

[10]  Yongbo Li,et al.  Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform , 2017 .

[11]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[12]  I. Soltani Bozchalooi,et al.  An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection ☆ , 2010 .

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

[14]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[15]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[16]  Zechao Liu,et al.  Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet , 2020 .

[17]  Dong Wang,et al.  Theoretical investigation of the upper and lower bounds of a generalized dimensionless bearing health indicator , 2018 .

[18]  Zepeng Liu,et al.  A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings , 2020 .

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

[20]  Chunhua Zhao,et al.  Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing , 2019, Measurement.

[21]  Ming Liang,et al.  Auto-OBSD: Automatic parameter selection for reliable Oscillatory Behavior-based Signal Decomposition with an application to bearing fault signature extraction , 2017 .

[22]  Ivan W. Selesnick,et al.  Wavelet Transform With Tunable Q-Factor , 2011, IEEE Transactions on Signal Processing.

[23]  Cheng Zhang,et al.  Transient extraction based on minimax concave regularized sparse representation for gear fault diagnosis , 2020 .

[24]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Weiwei Qian,et al.  A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals , 2020 .

[26]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

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

[28]  Anil Kumar,et al.  Tunable Q-factor Wavelet Transform for Extraction of Weak Bursts in the Vibration Signal of an Angular Contact Bearing☆ , 2016 .

[29]  Shuting Wan,et al.  Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor , 2020 .

[30]  Ming Liang,et al.  A method for tachometer-free and resampling-free bearing fault diagnostics under time-varying speed conditions , 2019, Measurement.

[31]  Jiawei Xiang,et al.  Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects , 2021 .

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

[33]  Ivan W. Selesnick,et al.  Sparse signal representations using the tunable Q-factor wavelet transform , 2011, Optical Engineering + Applications.

[34]  Mengchen Shan,et al.  A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning , 2019 .

[35]  Juanjuan Shi,et al.  Iterative matching synchrosqueezing transform and application to rotating machinery fault diagnosis under nonstationary conditions , 2020 .

[36]  Jianfeng Ma,et al.  Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel-Ziv , 2018, Measurement.

[37]  Dong Wang,et al.  Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients , 2018 .

[38]  Jiawei Xiang,et al.  Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images , 2020 .

[39]  T.G. Habetler,et al.  Fault-signature modeling and detection of inner-race bearing faults , 2006, IEEE Transactions on Industry Applications.

[40]  Chang Liu,et al.  Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference , 2021 .

[41]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[42]  M. Arif,et al.  Multiscale Permutation Entropy of Physiological Time Series , 2005, 2005 Pakistan Section Multitopic Conference.

[43]  Jiawei Xiang,et al.  Improved CNN for the diagnosis of engine defects of 2-wheeler vehicle using wavelet synchro-squeezed transform (WSST) , 2020, Knowl. Based Syst..

[44]  Gaigai Cai,et al.  Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method , 2015 .

[45]  Pavan Kumar Kankar,et al.  Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier , 2015 .

[46]  Zhike Peng,et al.  A scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy , 2020, Measurement Science and Technology.

[47]  Ayyaz Hussain,et al.  Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features , 2017, IEEE Access.

[48]  Thomas R. Kurfess,et al.  Rolling element bearing diagnostics in run-to-failure lifetime testing , 2001 .