Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing

Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multiscale fuzzy entropy (CIMFE) is proposed by using cubic spline interpolation of the time series over different scales to overcome the drawbacks of the coarse-grained MFE process. The proposed CIMFE method is compared with MSE and MFE by analyzing simulation signals and the result indicates that CIMFE is more robust than MSE and MFE in analyzing short time series. Taking this into account, a new fault diagnosis method for rolling bearing is presented by combining CIMFE for feature extraction with Laplacian support vector machine for fault feature classification. Finally, the proposed fault diagnosis method is applied to the experiment data of rolling bearing by comparing with the MSE, MFE and other existing methods, and the recognition rate of the proposed method is 98.71%, 98.71%, 98.71%, 98.71% and 100% under different training samples (5, 10, 15, 20 and 25), which is higher than that of the existing methods.

[1]  Pablo Alvarez,et al.  Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis , 2019, Entropy.

[2]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

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

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

[5]  Junyan Yang,et al.  Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension , 2007 .

[6]  V. Yeragani,et al.  Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues. , 1998, Clinical science.

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

[8]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[9]  Minqiang Xu,et al.  Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis , 2016 .

[10]  Jiang Wu,et al.  A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis , 2009, Interdisciplinary Sciences: Computational Life Sciences.

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

[12]  Pablo Alvarez,et al.  Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis , 2019, Entropy.

[13]  Li Ma,et al.  A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD , 2014 .

[14]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[15]  Junsheng Cheng,et al.  Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis , 2014 .

[16]  Nibaldo Rodríguez,et al.  Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis , 2017, Entropy.

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

[18]  Stefano Di Gennaro,et al.  Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery , 2017 .

[19]  Joo-Hyung Kim,et al.  Fault diagnosis of rotating machine by thermography method on support vector machine , 2014 .

[20]  Minghong Han,et al.  A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings , 2015 .

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

[22]  Fulei Chu,et al.  Support vector machines-based fault diagnosis for turbo-pump rotor , 2006 .

[23]  Li Ma,et al.  A fault diagnosis approach for roller bearing based on improved intrinsic timescale decomposition de-noising and kriging-variable predictive model-based class discriminate , 2016 .

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

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

[26]  Haiyang Pan,et al.  Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines , 2017 .

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

[28]  Hong-Bo Xie,et al.  Complexity analysis of the biomedical signal using fuzzy entropy measurement , 2011, Appl. Soft Comput..

[29]  Chun-Chieh Wang,et al.  Time Series Analysis Using Composite Multiscale Entropy , 2013, Entropy.

[30]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.