Application of EEMD and improved frequency band entropy in bearing fault feature extraction.

Ensemble empirical mode decomposition (EEMD) is widely used in condition monitoring of modern machine for its unique advantages. However, when the signal-to-noise ratio is low, the de-noising function of it is often not ideal. Thus, a new fault feature extraction method for rolling bearing combining EEMD and improved frequency band entropy (IFBE) is proposed, i.e., EEMD-IFBE. According to the problem of multiple intrinsic mode functions (IMFs) generated by EEMD, how to select the sensitive IMF(s) that can better reflect fault characteristics, a novel method based on FBE for sensitive IMF is proposed. In addition, since the bandwidth parameter is set empirically when the band-pass filter is designed based on the original FBE, a novel bandwidth parameter optimization method based on the principle of maximum envelope kurtosis is proposed. First, the original vibration signal is subjected to EEMD to obtain a series of IMFs; Then, the FBE values are obtained for the original signal and each IMF component, and the bandwidth of the band-pass filter (empirically) is designed as the characteristic frequency band at the minimum entropy value, and the affiliation between the characteristic frequency band of each IMF and the characteristic frequency band of the original signal is compared, and then selecting the sensitive IMF(s) that reflects the characteristics of the fault; Third, due to the influence of background noise, it is difficult to accurately obtain the fault frequency from the selected IMF(s). Therefore, the band-pass filter designed based on FBE is used, and the bandwidth parameter is optimized based on the principle of envelope kurtosis maximum, and then the selected sensitive IMF is band-pass filtered. Finally, the envelope power spectrum analysis is performed on the filtered signal to extract the fault characteristic frequency, and then the fault diagnosis of the bearing is realized. The method is successfully applied to simulated data and actual data of rolling bearing, which can accurately diagnose fault characteristics of bearing and prove the effectiveness and advantages of the method.

[1]  Mohamed Benbouzid,et al.  EEMD-based notch filter for induction machine bearing faults detection , 2018 .

[2]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[3]  Zhang Shicheng.  Experimental Study on Mechanics Properties of Rubber for Packer Sleeve , 2011 .

[4]  Hee-Jun Kang,et al.  Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization , 2016, IEEE Transactions on Industrial Informatics.

[5]  Bing Li,et al.  Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform , 2015 .

[6]  Zhixing Li,et al.  Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance , 2016 .

[7]  Jun Hu,et al.  Fault Diagnosis System of Rotating Machinery Vibration Signal , 2011 .

[8]  Lei Yaguo,et al.  Machinery Fault Diagnosis Based on Improved Hilbert-Huang Transform , 2011 .

[9]  Hua Li,et al.  Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy , 2019, Mechanical Systems and Signal Processing.

[10]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

[11]  Gabriel Rilling,et al.  on the Influence of Sampling on the Empirical Mode Decomposition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[12]  Wei Xu,et al.  Application of Single Channel Blind Separation Algorithm Based on EEMD-PCA-RobustICA in Bearing Fault Diagnosis , 2017 .

[13]  M. Benbouzid,et al.  EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component , 2013 .

[14]  Ivan Prebil,et al.  EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis , 2016 .

[15]  Satinder Singh,et al.  Bearing damage assessment using Jensen-Rényi Divergence based on EEMD , 2017 .

[16]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[17]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[18]  Akhand Rai,et al.  An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence , 2018, Appl. Soft Comput..

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

[20]  Wei Li,et al.  Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis , 2014 .

[21]  Wei Hou,et al.  Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation , 2014 .

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

[23]  Te Han,et al.  The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value , 2016 .

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

[25]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

[26]  J. Antoni.  Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions , 2007 .

[27]  Xia Wang,et al.  Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension , 2013 .

[28]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[29]  Robert X. Gao,et al.  Integration of EEMD and ICA for wind turbine gearbox diagnosis , 2014 .

[30]  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.

[31]  Dongying Han,et al.  Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD , 2016 .

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

[33]  Q. Du,et al.  Application of the EMD method in the vibration analysis of ball bearings , 2007 .

[34]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[35]  Yifei Tong,et al.  A UV-visible absorption spectrum denoising method based on EEMD and an improved universal threshold filter , 2018, RSC advances.

[36]  Chengwei Li,et al.  Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement , 2015 .

[37]  Farhat Fnaiech,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[38]  Hu Xiao,et al.  Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance , 2015 .

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

[40]  Jimeng Li,et al.  Adaptive Multiscale Noise Control Enhanced Stochastic Resonance Method Based on Modified EEMD with Its Application in Bearing Fault Diagnosis , 2016 .

[41]  Bong-Hwan Koh,et al.  Fault Detection of Bearing Systems through EEMD and Optimization Algorithm , 2017, Sensors.

[42]  Tang Gui-ji.  Rolling Bearing Fault Feature Extraction Method Based on Ensemble Empirical Mode Decomposition and Kurtosis Criterion , 2012 .

[43]  Yun Wang,et al.  Fault Feature Selection and Diagnosis of Rolling Bearings Based on EEMD and Optimized Elman_AdaBoost Algorithm , 2018, IEEE Sensors Journal.