Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

Abstract:

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

• Periodical: Applied Mechanics and Materials (Volume 618)
• Pages: 458-462
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.618.458
• Online since: August 2014
• Authors: Gang Yu*, Ye Chen
• Keywords: Alpha Stable Distribution, Sliding Widows, Stochastic Resonance

References

[1] Q.B. He, R.Q. Yan, F.R. Kong, R.X. Du, Machine condition monitoring using principal component representations, Mech. Syst. Signal Process. 23 (2009) 446–466.

DOI: 10.1016/j.ymssp.2008.03.010

[2] N. Sawalhi, R.B. Randall, Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniquesto track the spall size, Mech. Syst. Signal Process. 25 (2011) 846–870.

DOI: 10.1016/j.ymssp.2010.09.009

[3] N. Li, R. Zhou, Q.H. Hu, X.H. Liu, Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine, Mech. Syst Signal Process. 28 (2012) 608–621.

DOI: 10.1016/j.ymssp.2011.10.016

[4] S.B. Wang, W.G. Huang, Z.K. Zhu, Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis, Mech. Syst. Signal Process. 25 (2011) 1299–1320.

DOI: 10.1016/j.ymssp.2010.10.013

[5] R.J. Hao, Z.K. Peng, Z.P. Feng, F.L. Chu, Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings, Meas. Sci. Technol. 22 (4) (2011). 045708_1–045708_13.

DOI: 10.1088/0957-0233/22/4/045708

[6] Y. Yang, J.S. Cheng, K. Zhang, An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems, Measurement 45 (3) (2012) 561–570.

DOI: 10.1016/j.measurement.2011.10.010

[7] Jimeng Li, Xuefeng Chen, Zhengjia He, Adaptive stochastic resonance method for impact signal detection based on sliding window, Mechanical Systems and Signal Processing36 (2013) 240–255.

DOI: 10.1016/j.ymssp.2012.12.004

[8] R. Benzi, A. Sutera, A. Vulpiana, The mechanism of stochastic resonance, J. Phys. A: Math. Gen. 14 (11) (1981) 453–457.

[9] R. Benzi, G. Parisi, A. Sutera, A. Vulpiani, Stochastic resonance in climatic change, Tellus Ser. B 34 (1) (1982) 10–16.

DOI: 10.1111/j.2153-3490.1982.tb01787.x

[10] J.J. Collins, C.C. Chow, A.C. Capela, T.T. Imhoff, Aperiodic stochastic resonance, Phys. Rev. E 54 (5) (1996) 5575–5584.

DOI: 10.1103/physreve.54.5575

[11] Y.G. Leng, T.Y. Wang, Y. Guo, Y.G. Xu, S.B. Fan, Engineering signal processing based on bistable stochastic resonance, Mech. Syst. Signal Process. 12(1) (2007) 138–150.

DOI: 10.1016/j.ymssp.2005.08.002