基于隐Markov树的序贯故障诊断模型  

Sequential Fault Diagnosis Model based on Hidden Markov Tree

作　　者：桂林[1] 武小悦[1] 

机构地区：[1]国防科技大学信息系统与管理学院,湖南长沙410073

出　　处：《火力与指挥控制》2009年第4期104-107,110,共5页Fire Control & Command Control

基　　金：国家自然科学基金(70571083);湖南省自然科学基金资助项目(07JJ3133)

摘　　要：为了充分利用先验信息和实测信号,提高故障识别率,根据Bayes方法和序贯决策的思想,将实测信号分段,将前一段信号的诊断后验信息作为后一段信号的先验信息,提出了一种基于隐Markov树(hidden Markov tree,HMT)的序贯故障诊断模型。给出了诊断模型的建模步骤、HMT模型的建立方法和Bayes后验概率的计算方法。将模型应用于某型减速器故障诊断的结果表明,对于有先验信息和无先验情况,该序贯模型都可以有效地提高故障识别率。To fully utilize prior information and on-line signals in order to enhance fault recognition rate, following Bayesian method and the sequential decision thought, a sequential fault diagnosis model based on Hidden Markov Tree （HMT） is presented in this paper. On -line signals are divided into segments, and posterior of one segment is used as the prior for next segment. The diagnosis modeling procedure, modeling method for HMT and computational methods for Bayesian posterior probability are also given. The application of the presented methods to a gearbox fault diagnosis shows that the sequential model can effectively improve fault identification rate in situations either having prior or with no prior information.

关 键 词：隐MARKOV树 先验信息 序贯 故障诊断 减速器 

分 类 号：TH165[机械工程—机械制造及自动化]