A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

[1]  G. Keller,et al.  Entropy of interval maps via permutations , 2002 .

[2]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

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

[4]  José Francisco Gómez-Aguilar,et al.  Parameter identification of periodical signals: Application to measurement and analysis of ocean wave forces , 2017, Digit. Signal Process..

[5]  Hamed Azami,et al.  Application of dispersion entropy to status characterization of rotary machines , 2019, Journal of Sound and Vibration.

[6]  Hamed Azami,et al.  Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals , 2016, IEEE Transactions on Biomedical Engineering.

[7]  Wei Sun,et al.  Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China , 2016 .

[8]  Jian-Jiun Ding,et al.  Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine , 2012, Entropy.

[9]  Yanhe Xu,et al.  A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy , 2019, Sustainability.

[10]  Francesco Villecco,et al.  On the Evaluation of Errors in the Virtual Design of Mechanical Systems , 2018, Machines.

[11]  Hong Yang,et al.  Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising , 2018, Entropy.

[12]  Minping Jia,et al.  Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection , 2019, Knowl. Based Syst..

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

[14]  Xiaoming Xue,et al.  Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests , 2019, Entropy.

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

[16]  Francesco Villecco,et al.  Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis , 2017, Entropy.

[17]  Wei Jiang,et al.  A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder. , 2019, ISA transactions.

[18]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[19]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[20]  Hu,et al.  Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy , 2019, Applied Sciences.

[21]  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.

[22]  Kai Wang,et al.  Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM , 2019, Energy Conversion and Management.

[23]  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 .

[24]  Jian Xiao,et al.  Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine , 2014 .

[25]  Hao Zhong,et al.  Vibration trend measurement for a hydropower generator based on optimal variational mode decomposition and an LSSVM improved with chaotic sine cosine algorithm optimization , 2018, Measurement Science and Technology.

[26]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

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

[28]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[29]  Yung-Hung Wang,et al.  On the computational complexity of the empirical mode decomposition algorithm , 2014 .

[30]  Minqiang Xu,et al.  A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .

[31]  Kai Wang,et al.  Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery , 2019, Complex..

[32]  Guiji Tang,et al.  Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping , 2019, Entropy.

[33]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[34]  Diego Cabrera,et al.  Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .

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

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

[37]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[38]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[40]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[41]  Hamed Azami,et al.  Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.

[42]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[43]  Vicenç Puig,et al.  Diagnosis of Fluid Leaks in Pipelines Using Dynamic PCA , 2018 .

[44]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[45]  Bok-Suk Shin,et al.  Accurate and Robust Line Segment Extraction Using Minimum Entropy With Hough Transform , 2015, IEEE Transactions on Image Processing.

[46]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.