Fault Diagnosis of Rolling Bearing Based on Dual-Tree Complex Wavelet Transform and AR Power Spectrum

Abstract:

Aiming at the strong background noise involved in the signals of rolling bearing and the difficulty to extract fault feature in practice, a new fault diagnosis method is proposed based on Dual-tree Complex Wavelet Transform (DT-CWT) and AR power spectrum. Firstly, the non-stationary and complex vibration signal is decomposed into several different frequency band components through dual-tree complex wavelet decomposition; Secondly, Hilbert envelope is formed from the components which contains the fault information. Finally, the auto-power spectrum can be obtained by auto-regressive (AR) spectrum. The noise interference was eliminated effectively, and the effective signal information was retained at the same time. Thus, the fault feature information was extracted. In this paper, the fault test and the engineering practical fault data of rolling bearing were analyzed by dual-tree complex wavelet transform and AR power spectrum. The results show that the noise of the vibration signal was eliminated effectively, and the fault feature were extracted. The feasibility and effectiveness of the method were verified.

• Periodical: Advanced Materials Research (Volume 819)
• Pages: 271-276
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.819.271
• Online since: September 2013
• Authors: Zhi Peng Meng, Yonggang Xu, Guo Liang Zhao, Sheng Fu
• Keywords: AR Power Spectrum, Dual-Tree Complex Wavelet Transform, Fault Diagnosis, Hilbert Envelope

References

[1] Peng Zhike, Chu Fulei, He Yongyong. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram[J]. Journal of Sound and Vibration, 2002, 253(5): 1087-1100.

DOI: 10.1006/jsvi.2001.4085

[2] Kingsbury N G. The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters[J]. IEEE Digital Signal Processing Workshop, 1998, 98(1): 2-5.

[3] Selesnick I W, Baraniuk R G, Kingsbury N G. The dual-tree complex wavelet transform[J]. IEEE Digital Signal Processing Magazine, 2005, 22(6): 123-151.

DOI: 10.1109/msp.2005.1550194

[4] Edward H S L, Pickering M R, Frater M R, et al. Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform[J]. Image and Vision Computing, 2011, 9(1): 15-28.

DOI: 10.1016/j.imavis.2010.08.004

[5] Priyaa K J, Rajeshb R S. Local fusion of complex dual-tree wavelet coefficients based face recognition for single sample problem[J]. Procedia Computer Science, 2010, 2(1): 94-100.

DOI: 10.1016/j.procs.2010.11.012

[6] Wang Na, Zheng Dezhong, Liu Yonghong. New method for speech enhancement based on dual tree complex wavelet packet transform[J]. Journal of Sensore and Actuators, 2009, 22(7): 983-987.

[7] Wang Yanxue, He Zhengjia, Zi Yanyang. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform[J]. Mechanical Systems and Signal Processing, 2010, 24(1): 119-13.

DOI: 10.1016/j.ymssp.2009.06.015

[8] Chen Zhixin, Xu Jinwu, Yang Debin. Denoising method of block thresholding based on DT-CWT and its application in mechanical fault diagnosis[J]. Journal of Mechanical Engineering, 2007, 43(6): 200-204.

DOI: 10.3901/jme.2007.06.200

[9] Su Wensheng, Wang Fengtao, Zhu Hong, et al. Denoising method based on hidden Markov tree model in dual tree complex wavelet domain and its application in mechanical fault diagnosis[J]. Journal of Vibration and Shock, 2011, 30(6): 47-52.

[10] Cheng Junsheng, Yu Dejie, Ynag Yu. A fault diagnosis approach for roller bearings based on EMD method and AR model[J]. Mechanical Systems and Signal Processing, 2006, 20( 2): 350-362.

DOI: 10.1016/j.ymssp.2004.11.002