Fault Diagnosis of Rolling Bearing Based on Improved Extreme Learning Machine

Authors

Tiantian Wang, 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: According to the parameter-dependent characteristics of the wavelet kernel extreme learning machine, which make the effect of the rolling bearing fault classifier model poor, a fault classification method based on improved grey wolf optimizer algorithm for optimizing wavelet kernel extreme learning machine was proposed. The method combined the variational mode decomposition and singular value decomposition to extract fault signal characteristics. The opposition-based-learning and the levy flight strategy were introduced to improve the grey wolf optimizer algorithm, which enriched the population diversity of the grey wolf optimizer algorithm, improved the convergence speed of the algorithm and the ability to get out of the local optimum. The improved grey wolf optimizer algorithm was applied to optimize the parameters of wavelet kernel extreme learning machine, and the best parameter combination was obtained to build the classifier model. The comparative experimental results show that the method has better fault recognition effect, faster training speed and stronger stability.

Recommended Citation

Wang, Tiantian; Yan, Wang; and Ji, Zhicheng (2019) "Fault Diagnosis of Rolling Bearing Based on Improved Extreme Learning Machine," Journal of System Simulation: Vol. 30: Iss. 11, Article 43.
DOI: 10.16182/j.issn1004731x.joss.201811043
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/43

First Page

4413

Revised Date

2018-06-02

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811043

Last Page

4420

CLC

TP391.9

Recommended Citation

Wang Tiantian, Wang Yan, Ji Zhicheng. Fault Diagnosis of Rolling Bearing Based on Improved Extreme Learning Machine[J]. Journal of System Simulation, 2018, 30(11): 4413-4420.

DOI

10.16182/j.issn1004731x.joss.201811043