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The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

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School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
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State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
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Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an 710049, China
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School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
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Authors to whom correspondence should be addressed.
Sensors 2014, 14(8), 13692-13707; https://doi.org/10.3390/s140813692
Received: 15 May 2014 / Revised: 1 July 2014 / Accepted: 18 July 2014 / Published: 29 July 2014
(This article belongs to the Section Physical Sensors)
In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test. View Full-Text
Keywords: weak impulsive signals; parameter-tuning stochastic resonance; moving least squares fitting; recovery weak impulsive signals; parameter-tuning stochastic resonance; moving least squares fitting; recovery
MDPI and ACS Style

Jiang, K.; Xu, G.; Liang, L.; Tao, T.; Gu, F. The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting. Sensors 2014, 14, 13692-13707.

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