张小强,朱文辉,康铁宇,黄晋英.基于人工免疫算法的离散隐马尔科夫故障诊断模型优化[J].装备环境工程,2019,16(1):63-67. ZHANG Xiao-qiang,ZHU Wen-hui,KANG Tie-yu,HUANG Jin-ying.Optimization of Discrete Hidden Markov Fault Diagnosis Model Based on Artificial Immune Algorithm[J].Equipment Environmental Engineering,2019,16(1):63-67.
基于人工免疫算法的离散隐马尔科夫故障诊断模型优化
Optimization of Discrete Hidden Markov Fault Diagnosis Model Based on Artificial Immune Algorithm
投稿时间:2018-10-15  修订日期:2019-01-25
DOI:10.7643/ issn.1672-9242.2019.01.012
中文关键词:  故障诊断  离散隐马尔科夫模型  Discrete Hidden Markov Model(DHMM)  人工免疫优化  行星齿轮箱
英文关键词:fault diagnosis  discrete hidden Markov model  artificial immune optimization  planetary gearbox
基金项目:
作者单位
张小强 1.北京北方车辆集团有限公司,北京 100072 
朱文辉 2. 中北大学 机械工程学院,太原 030051 
康铁宇 1.北京北方车辆集团有限公司,北京 100072 
黄晋英 2. 中北大学 机械工程学院,太原 030051 
AuthorInstitution
ZHANG Xiao-qiang 1. Beijing North Vehicle Group Corporation, Beijing 100072, China 
ZHU Wen-hui 2. School of Mechanical Engineering, North University of China, Taiyuan 030051, China 
KANG Tie-yu 1. Beijing North Vehicle Group Corporation, Beijing 100072, China 
HUANG Jin-ying 2. School of Mechanical Engineering, North University of China, Taiyuan 030051, China 
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中文摘要:
      目的 解决离散隐马尔科夫模型在行星齿轮箱故障诊断中的自适应性和泛化性问题。方法 建立人工免疫优化模型,将包含易被误判样本的多样本集作为抗原,以其正确识别率为适应度函数,不断对初始观测矩阵进行增殖、变异和选择,获得识别率最高时的初始观测矩阵,利用人工免疫算法对隐马尔科夫故障诊断模型的初始观测矩阵进行优化。结果 将该方法应用于行星齿轮箱的故障诊断中,通过不同工况下的对比试验、单样本和多样本优化对比试验,验证了优化后的隐马尔科夫故障诊断模型的诊断效果。结论 优化后的隐马尔科夫故障诊断模型具有更好的适应性,诊断精度显著提高。
英文摘要:
      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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