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Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

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  • Shuting Wan
  • Xiong Zhang
  • Longjiang Dou

Abstract

The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature by the envelope demodulation method. However, in practical applications, the fault source may be located in different resonant frequency bands; plus in noise interference, the weak side of the compound fault is not easy to be identified by the FSK. In order to improve the accuracy of fast spectral kurtosis analysis method, a modified method based on maximum correlation kurtosis deconvolution (MCKD) is proposed. According to the possible fault characteristic frequencies, the period of MCKD is calculated, and the appropriate filter length is selected to filter the original compound fault signal. In this way, the compound fault located in different resonance bands is separated. Then, the signal after MCKD filtering is analyzed by FSK. Through the simulation and experimental analysis, the MCKD can separate the compound fault information in different frequency band and eliminate the noise interference; the FSK can accurately identify the resonance frequency and identify the weak fault characteristics of compound fault.

Suggested Citation

  • Shuting Wan & Xiong Zhang & Longjiang Dou, 2018. "Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, March.
    • Handle: RePEc:hin:jnlmpe:6513045
    DOI: 10.1155/2018/6513045
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    Cited by:

    1. Wangpeng He & Peipei Zhang & Xuan Liu & Binqiang Chen & Baolong Guo, 2022. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis," Sustainability, MDPI, vol. 14(24), pages 1-15, December.

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