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Articles:2016,Vol:21,Issue(4):202-216 |
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Citation: |
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WANG Ming-yue, MIAO Bing-rong, YUAN Cheng-biao. Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy[J]. International Journal of Plant Engineering and Management, 2016, 21(4): 202-216 |
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| Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy |
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| WANG Ming-yue, MIAO Bing-rong, YUAN Cheng-biao |
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| Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, P. R. China |
| Abstract: |
| Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition (WPD) and local mean decomposition (LMD) permutation entropy, which is based on the support vector machine (SVM) as the feature vector pattern recognition device. Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions (PE) by the local mean decomposition (LMD), and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing. The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy. |
| Key words:
fault diagnosis
wavelet packet decomposition(WPD)
local mean decomposition (LMD)
permutation entropy
support vector machine(SVM)
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| Received: 2016-09-20
Revised:
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| DOI: 10.13434/j.cnki.1007-4546.2016.0402 |
| Funds: This work was supported by the National Natural Science Foundation of China (51375405); Independent Project of the State Key Laboratory of Traction Power (2016TP_10) |
| Corresponding author:
Email： |
| Author description:
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References: |
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[1] Ma J, Wu J, Fan Y, et al. The rolling bearing fault feature extraction based on the LMD and envelope demodulation[J]. Mathematical Problems in Engineering, 2015(6):1-13
[2] Tian Y, Wang Z, Lu C. Self-adaptive bearing fault diagnosis based on permutation entropy and manifold-based dynamic time warping[J]. Mechanical Systems & Signal Processing, 2016
[3] Cheng S J, Shi M L, Yang Y. Roller bearing fault diagnosis method based on LMD and neural network[J]. Journal of Vibration and Shock, 2010. 29(8):141-144
[4] Yan T X, Zhang R L, Wang T, et al. Research of bearing compound fault diagnosis based on EEMD and Hilbert envelope analysis[J]. Journal of Mechanical Transmission, 2016,(6):132-135(in Chinese)
[5] Feng F Z, Rao G Q, Si A W, et al. Research and application of the arithmetic of PE in testing the sudden change of vibration signal[J]. Journal of Vibration Engineering, 2012,25(2):221-224
[6] Zheng J D, Cheng J S, Yang Y. A rolling bearing fault diagnosis method based on LMD and permutation entropy[J]. Journal of Vibration,Measurement & Diagnosis, 2014,34(5):802-806(in Chinese)
[7] Cheng J S, Ma X W, Yang Y. Rolling bearing fault diagnosis method based on permutation entropy and VPMCD[J]. Journal of Vibration and Shock, 2014,33(11):119-123(in Chinese)
[8] Zhou T T, Zhu X M, Peng W C. A wavelet threshold denoising method for fault data based on CEEMD and permutation entropy[J]. Journal of Vibration and Shock, 2015,34(23):207-211(in Chinese)
[9] Sun W, Xiong B S, Huang J Q, et al. Fault diagnosis of a rolling bearing using wavelet packet denoising and LMD[J]. Journal of Vibration and Shock, 2012,31(18):153-156(in Chinese)
[10] Yang W Z, Ma W S, Ren X Q. Application of wavelet packet denoising method in fault diagnosis of bearings[J]. Noise and Vibration Control, 2009,29(4):50-53(in Chinese)
[11] Widodo A, Shim M C, Caesarendra W, et al. Intelligent prognostics for battery health monitoring based on sample entropy[J]. Expert Systems with Applications, 2011,38(9):11763-11769(in Chinese)
[12] Zhang X T, Tang L W, Wang P. The arrival identification of acoustic emission signal based on permutation entropy[J]. Modern Manufacturing Engineering, 2015,(5):126-130
[13] Zheng J D, Cheng J S, Yang Y. Multi-scale Permutation entropy and its application to rolling bearing fault diagnosis[J]. China Mechanical Engineering, 2013,(19):2641-2646(in Chinese)
[14] Feng F Z, Rao G Q, Zhang L X. Research on abnormality detection method for bearings based on EMD and permutation entropy[J]. Bearing, 2013,(2):53-56(in Chinese) |
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