基于HMM模型的滚动轴承故障分析  

Failure Analysis of Rolling Bearings Based on Hidden Markov Model

作　　者：刘小成[1] 孙健[1] 

机构地区：[1]华东理工大学机械与动力工程学院,上海200237

出　　处：《微计算机信息》2010年第16期122-123,149,共3页Control & Automation

摘　　要：将声信号作为故障诊断的主要信号源,结合隐马尔可夫理论,实现了对滚动轴承零件的故障诊断。首先建立不同滚动轴承的HMM模型,接着输入任意进行过滤波及矢量量化前期处理的机械故障声信号特征矢量集进行故障识别,处理过程应用MATLAB语言实现。Get acoustic signals as the main signal source of failure diagnosis with hidden Markov theory; the rolling bearing fault diagnosis could be feasible. First, HMM models of different kinds of roiling bearings should be established, and then inputted any mechanical failure acoustic signals＇ feature vector set, which is filtered and pre-processed in vector quantization. All the process used MATLAB language.

关 键 词：隐马尔可夫模型(HMM) 滚动轴承 声信号 故障诊断系统 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]