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Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing

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Abstract

The fault diagnosis of wind turbine bearings is challenging because of the heavy background noise and changes in wind speed. Stochastic resonance (SR) is an effective method of detecting fault signal from noise. However, the benefit of SR is seriously limited by the system parameters and the frequency of the input signal. A novel fault diagnosis method for wind turbine bearings, combining an adaptive SR algorithm that is based on quantum particle swarm optimization (QPSO) and frequency conversion based on frequency information exchange (FIE), is proposed. First, the frequency information of the fault characteristic signal is exchanged with the reference frequency by FIE, which can eliminate the limitation of the frequency band. Then, the SR system parameters are optimized by QPSO to avoid blind parameter selection. The signal after FIE is processed by the optimized SR system. The results of case study show that under the same input signal, the proposed method can achieve better signal-to-noise ratio and response amplitude than can the traditional double-side band modulation method and an SR method that is combined only with an optimization algorithm.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Xiaojiao Gu & Changzheng Chen

  2. Liaoning Engineering Center for Vibration and Noise Control, Shenyang, 110870, China

    Changzheng Chen

Authors
  1. Xiaojiao Gu
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  2. Changzheng Chen
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Corresponding author

Correspondence to Changzheng Chen.

Additional information

Recommended by Associate Editor Gyuhae Park

Xiaojiao Gu received her M.S. in mechanical engineering from Shenyang University of Technology, China, in 2015. She is currently a Ph.D. student at the School of Mechanical Engineering, Shenyang University of Technology, China. Her research interests are wind turbine condition monitoring and fault diagnosis.

Changzheng Chen is currently Professor and a Ph.D. supervisor at the School of Mechanical Engineering, Shenyang University of Technology, China. His current research interests include vibration, noise, and fault diagnosis.

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Gu, X., Chen, C. Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing. J Mech Sci Technol 33, 1007–1018 (2019). https://doi.org/10.1007/s12206-019-0202-8

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  • DOI: https://doi.org/10.1007/s12206-019-0202-8

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