Affiliation:
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, P. R. China
2. Department of Computer, Shijiazhuang Tiedao University Sifang College, Shijiazhuang 051132, P. R. China
Abstract
With the development of industry, the fault diagnosis requirements for rolling bearings are getting higher and higher. This paper aims to develop low-complexity solutions for bearing fault diagnosis. In this paper, we use wavelet decomposition to obtain gesture Monitoring Index Vector (MIVs), after this, an improved Hidden Markov Model (HMM) algorithm was proposed for bearing fault diagnosis, in which we apply the Genetic Algorithm (GA) to avoid the convergence to local optimum, thus improving the recognition performance. The experimental results on 11 groups of test datasets demonstrate that the proposed algorithm (GAHMM) can achieve a higher average recognition rate of 93%, 87%, 87%, 93%, 93%, 97%, 100%, 97%, 97%, 100%, 97%.
Funder
the Beijing Natural Science Foundation
the Fundamental Research Funds for the Central Universities
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
3 articles.
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