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Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD

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Abstract

The fast spectrum kurtosis (FSK) algorithm can adaptively identify resonance bands of a signal, and fault characteristics can be extracted by analyzing the selected frequency bands. However, in practical applications, the bearing failure may be composed of various faults (inner ring/outer ring/rolling element) and the faults may be located in different resonant bands. Due to the interference between different fault components and noise, the weak components may be submerged when FSK is used to deal with compound fault signals. To improve the accuracy of an FSK processing compound fault located in different resonance bands, an improved FSK method combined with the variational mode decomposition (VMD) is proposed. First, the parameters (number of components K / penalty factor α ) in the VMD decomposition are selected, and the original compound fault signal is preprocessed by VMD decomposition, so that the original signal is decomposed into K variational intrinsic mode function (VIMF) components. The resonance center bands of these signals are different from each other, so the different fault information is located in different VIMF. Finally, each VIMF component is calculated by FSK. Through the simulated and experimental analysis, the method can accurately identify the resonance bands, and identify the weak fault characteristics of compound bearing fault.

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Authors and Affiliations

  1. Department of Mechanical Engineering, North China Electric Power University, Baoding, 071003, China

    Shuting Wan, Xiong Zhang & Longjiang Dou

Authors
  1. Shuting Wan
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  2. Xiong Zhang
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  3. Longjiang Dou
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Corresponding author

Correspondence to Xiong Zhang.

Additional information

Recommended by Associate Editor Gyuhae Park

Shuting Wan is a Professor at the School of Mechanical Engineering in North China Electric Power University. He obtained his Ph.D. from the North China Electric Power University, China, in 2006. His research interests include condition monitoring and fault diagnosis of power equipment.

Xiong Zhang is a Ph.D. student at the School of Mechanical Engineering in North China Electric Power University. His research interest is condition monitoring and fault diagnosis of power equipment.

Longjiang Dou is a Ph.D. student at the School of Mechanical Engineering in North China Electric Power University. His research interest is condition monitoring and fault diagnosis of power equipment.

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Wan, S., Zhang, X. & Dou, L. Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD. J Mech Sci Technol 32, 5189–5199 (2018). https://doi.org/10.1007/s12206-018-1017-8

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  • DOI: https://doi.org/10.1007/s12206-018-1017-8

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