Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm

Authors

Jiya Xu, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;

Abstract

Abstract: In order to recognize rolling bearing's fault types accurately according to the optimal characteristics of fault vibration signal of rolling bearing, a rolling bearing fault diagnosis method was proposed based on orthogonal matching pursuit algorithm and the optimized wavelet kernel extreme learning machine method. The OMP algorithm was used to de-noising the vibration signal of the bearing. The wavelet packet decomposition of the signal after de-noising was used to obtain the frequency band energy, and the fault characteristics were extracted. By using an improved whale optimization algorithm based on von-neumann, the penalty factor and kernel parameter of wavelet kernel extreme learning machine were optimized to design a classifier of rolling bearing's fault types. The experimental results prove that the proposed method can accurately and effectively identify the fault type.

Recommended Citation

Xu, Jiya; Yan, Wang; and Ji, Zhicheng (2020) "Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm," Journal of System Simulation: Vol. 29: Iss. 9, Article 43.
DOI: 10.16182/j.issn1004731x.joss.201709042
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss9/43

First Page

2189

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201709042

Last Page

2197

CLC

TP391.9

Recommended Citation

Xu Jiya, Wang Yan, Ji Zhicheng. Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J]. Journal of System Simulation, 2017, 29(9): 2189-2197.

DOI

10.16182/j.issn1004731x.joss.201709042