Extended stochastic resonance (SR) and its applications in weak mechanical signal processing

被引：16

作者：

Hu N. ^{[1
]}

Chen M. ^{[1
]}

Qin G. ^{[1
]}

Xia L. ^{[1
]}

Pan Z. ^{[1
]}

Feng Z. ^{[1
]}

机构：

[1] School of Mechatronics Engineering and Automation, National University of Defense Technology

来源：

Frontiers of Mechanical Engineering in China | 2009年 / 4卷 / 4期

基金：

中国国家自然科学基金;

关键词：

Envelope analysis; Extended stochastic resonance (SR); Incipient fault detection; Scale transform; Stability analysis of SR; Weak signal detection;

D O I：

10.1007/s11465-009-0072-3

中图分类号：

学科分类号：

摘要：

To catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. This paper presents a method based on stochastic resonance (SR) to detect weak fault signal. First, a discrete model of a bistable system that can demonstrate SR is researched, and the stability condition for controlling the selection of model parameters of the discrete model and guarantee the solving convergence are established. Then, the frequency range of the weak signals that the SR model can detect is extended through a type of normalized scale transformation. Finally, the method is applied to extract the weak characteristic component from heavy noise to indicate the little crack fault in a bearing outer circle. © Higher Education Press and Springer-Verlag 2009.

引用

页码：450 / 461

页数：11

共 9 条

[1]

Qu L.S., Lin J., A difference resonator for detecting weak signals, Journal of Measurement, 26, 1, pp. 69-77, (1999)

[2]

Donald L.B., Chaotic oscillators and CMFFNS for signal detection in noise environment, Proceedings of IEEE International Joint Conference on Neural Networks, 2, pp. 881-888, (1992)

[3]

Hu N.Q., Wen X.S., The application of duffing oscillator in characteristic signal detection of early fault, Journal of Sound and Vibration, 268, 5, pp. 917-931, (2003)

[4]

He Z.J., Zhao J.Y., Meng Q.F., Wavelet transform in tandem with autoregressive technique for monitoring and diagnosis of machinery, Chinese Journal of Mechanical Engineering, 9, 4, pp. 311-317, (1996)

[5]

Gong D.C., Hu G., Wen X.D., Yang C.Y., Qin G.R., Li R., Ding D.F., Experimental study of signal-to-noise ratio of stochastic resonance systems, Physical Review A, 46, 6, pp. 3243-3249, (1992)

[6]

Benzi R., Sutera A., Vulpiani A., The Mechanism of stochastic resonance, Journal of Physics A: Mathematical and General, 14, 11, pp. 453-457, (1981)

[7]

Gammaitoni L., Hanggi P., Jung P., Marchesoni F., Stochastic resonance, Reviews of Modern Physics, 70, 1, pp. 223-287, (1998)

[8]

Hu N.Q., Chen M., Wen X.S., The application of stochastic resonance theory for early detecting rub-impact fault of rotor system, Mechanical System and Signal Processing, 17, 4, pp. 883-895, (2003)

[9]

Franaszek M., Simiu E., Stochastic resonance: A chaotic dynamics approach, Physical Review E, 54, 2, pp. 1298-1304, (1996)

← 1 →