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| Optimization of Discrete Hidden Markov Fault Diagnosis Model Based on Artificial Immune Algorithm |
| Received:October 15, 2018 Revised:January 25, 2019 |
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| DOI：10.7643/ issn.1672-9242.2019.01.012 |
| KeyWord:fault diagnosis discrete hidden Markov model artificial immune optimization planetary gearbox |
| Author | Institution |
| ZHANG Xiao-qiang |
1. Beijing North Vehicle Group Corporation, Beijing , China |
| ZHU Wen-hui |
2. School of Mechanical Engineering, North University of China, Taiyuan , China |
| KANG Tie-yu |
1. Beijing North Vehicle Group Corporation, Beijing , China |
| HUANG Jin-ying |
2. School of Mechanical Engineering, North University of China, Taiyuan , China |
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| Abstract: |
| Objective To solve the adaptivity and generalization of discrete hidden Markov fault diagnosis model in planetary gearbox. Methods An artificial immune optimization model was established for the initial observation matrix of hidden Markov fault diagnosis model. To obtain the highest recognition rate, the multi-sample set containing the samples that are easily to be misjudged was used as the antigen. And the correct recognition rate was used as the fitness function. The initial observation matrix was continuously propagated, mutated and selected to obtain the highest recognition rate. The initial observation matrix of hidden Markov fault diagnosis model was optimized by the artificial immune algorithm. Results The method established was applied to the fault diagnosis of planetary gearbox. The diagnostic results of the optimized hidden Markov fault diagnosis model were verified by comparison test under different working conditions and single and multi-sample optimization comparison test. Conclusion The optimized hidden Markov fault diagnosis model has better adaptability and significant diagnostic accuracy. |
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