A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

[1]  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.

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

[3]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

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

[5]  Emine Ayaz,et al.  Feature extraction related to bearing damage in electric motors by wavelet analysis , 2003, J. Frankl. Inst..

[6]  Yang Yu,et al.  A roller bearing fault diagnosis method based on EMD energy entropy and ANN , 2006 .

[7]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[8]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Yu Yang,et al.  Local rub-impact fault diagnosis of the rotor systems based on EMD , 2009 .

[10]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[11]  Long Zhang,et al.  Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference , 2010, Expert Syst. Appl..

[12]  Alberto Bellini,et al.  Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison , 2008, IEEE Transactions on Industry Applications.

[13]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[14]  Zhang Yan.  Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine , 2012 .

[15]  Lin Liang,et al.  Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method , 2013 .

[16]  Chun-Chieh Wang,et al.  Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine , 2013, Entropy.

[17]  Chen Lu,et al.  Fault diagnosis and health assessment for bearings using the Mahalanobis–Taguchi system based on EMD-SVD , 2013 .

[18]  Dong Wang,et al.  A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture , 2013, Appl. Soft Comput..

[19]  Tao Liu,et al.  The fault detection and diagnosis in rolling element bearings using frequency band entropy , 2013 .

[20]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[21]  Humberto Henao,et al.  Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques , 2014, IEEE Industrial Electronics Magazine.

[22]  Junsheng Cheng,et al.  A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination , 2014 .

[23]  Qiang Liao,et al.  Hybrid fault-feature extraction of rolling element bearing via customized-lifting multi-wavelet packet transform , 2014 .

[24]  Zhihua Xia,et al.  Steganalysis of least significant bit matching using multi-order differences , 2014, Secur. Commun. Networks.

[25]  Minghong Han,et al.  A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings , 2014 .

[26]  Wei Li,et al.  Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features , 2014 .

[27]  Her-Terng Yau,et al.  Research and Development of a Chaotic Signal Synchronization Error Dynamics-Based Ball Bearing Fault Diagnostor , 2014, Entropy.

[28]  Bong-Hwan Koh,et al.  Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal , 2014, Sensors.

[29]  Keheng Zhu,et al.  A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm , 2014 .

[30]  Ling Shao,et al.  A rapid learning algorithm for vehicle classification , 2015, Inf. Sci..

[31]  Bin Gu,et al.  Incremental Support Vector Learning for Ordinal Regression , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[33]  Lorand Szabo,et al.  Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement , 2015, IEEE Transactions on Industrial Electronics.

[34]  P. K. Kankar,et al.  A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings , 2015 .

[35]  Zhongke Gao,et al.  The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy , 2015 .

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

[37]  Cong Wang,et al.  Non-negative EMD manifold for feature extraction in machinery fault diagnosis , 2015 .

[38]  Guanghua Xu,et al.  Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization , 2014, Chinese Journal of Mechanical Engineering.

[39]  Jin Chen,et al.  Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm , 2015 .

[40]  Enhua Wu,et al.  Robust dense reconstruction by range merging based on confidence estimation , 2016, Science China Information Sciences.

[41]  Feng Wu,et al.  Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform , 2015 .

[42]  Peter W. Tse,et al.  A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features , 2015 .

[43]  Bin Gu,et al.  Bi-Parameter Space Partition for Cost-Sensitive SVM , 2015, IJCAI.

[44]  Bin Gu,et al.  Incremental learning for ν-Support Vector Regression , 2015, Neural Networks.

[45]  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 .

[46]  Dongying Han,et al.  Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method , 2016 .

[47]  Zhixin Yang,et al.  A Hybrid EEMD-Based SampEn and SVD for Acoustic Signal Processing and Fault Diagnosis , 2016, Entropy.

[48]  Dan Liu,et al.  An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition , 2016 .

[49]  Xingming Sun,et al.  Structural Minimax Probability Machine , 2017, IEEE Transactions on Neural Networks and Learning Systems.