Case Studies for Bearing Fault Diagnosis based on Adaptive Myriad Filter and Alpha Stable Model

doi:10.23940/ijpe.17.04.p22.551555

Abstract

Abstract:

Bearing fault diagnosis is a key research content of condition-based maintenance for machineries. Because of noise interference, incipient bearing fault is always difficult to be found. Traditionally, a large number of filtering algorithms used are limited to the cases of Gaussian noise or linear operation. In this paper, the adaptive Myriad filter and alpha stable model are elaborated. Myriad filter is a non-linear filter, which can be effectively applied in impulsive environment. The order of Myriad filter can be determined by alpha stable model. Finally, both laboratory fault data and real fault data from a public data set are used to verify the efficiency of the proposed method.

Submitted on December 7, 2014; Revised on November 18, 2016; Accepted on April 23, 2017
References: 10

Xinghui Zhang, Fei Zhao, and Jianshe Kang. Case Studies for Bearing Fault Diagnosis based on Adaptive Myriad Filter and Alpha Stable Model [J]. Int J Performability Eng, 2017, 13(4): 551-555.

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References 0

	1. A. K. S. Jardine, D. Lin, and D. Banjevic, “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483-1510, October 2006.
	2. T. C. Kumar, G. Singh, and V. N. A. Naikan, “Broken Rotor Bar Fault Diagnosis in VFD Driven Induction Motors by an Improved Vibration Monitoring Technique,” International Journal of Performability Engineering, vol. 13, no. 1, pp. 87-94, January 2017.
	3. T. I. Patargias, C. T. Yiakopoulos, and I. A. Antoniadis, “Performance Assessment of a Morphological Index in Fault Prediction and Trending of Defective Rolling Element Bearings,” Nondestructive Testing and Evaluation, vol. 21, no. 1, pp. 39-60, August 2006.
	4. B. Li, P. L. Zhang, Z. J. Wang, S. S. Mi, and P. Y. Liu, “Morphological Covering based Generalized Dimension for Gear Fault Diagnosis,” Nonlinear Dynamics, vol. 67, no. 4, pp. 2561-2571, August 2012.
	5. Y. F. Li, X. H. Liang, and M. J. Zuo, “Diagonal Slice Spectrum Assisted Optimal Morphological Filter for Rolling Element Bearing Fault Diagnosis,” Mechanical Systems and Signal Processing, vol. 85, pp. 146-161, February 2017.
	6. L. J. Meng, J. W. Xiang, Y. X. Wang, Y. Y. Jiang, and H. F. Gao, “A Hybrid Fault Diagnosis Method using Morphological Filter-Translation Invariant Wavelet and Improved Ensemble Empirical Mode Decomposition,” Mechanical Systems and Signal Processing, vol. 50-51, pp. 101-115, January 2015.
	7. H. P. Li, J. M. Zhao, X. H. Zhang, and X. L. Ni, “A New Fault Diagnosis Method for Planetary Gearbox,” International Journal of Performability Engineering, vol. 12, no. 4, pp. 389-394, July 2016.
	8. J. G. Gonzalez and G. R. Arce, “Optimality of the Myriad Filter in Practical Impulsive-Noise Environments,’’ IEEE Transactions on Signal Processing, vol. 49, no.2, pp. 438-441, February 2001.
	9. G. A. Tsihrintzis and C. L. Nikias, “Fast Estimation of the Parameters of Alpha-Stable Impulsive Interference,” IEEE Transactions on Signal Processing, vol. 44, no. 6, pp. 1492-1503, June 1996.
	10. Fault Data Sets: http://www.mfpt.org/. Accessed 2013.