基于改进EEMD和HMM的采煤机摇臂轴承故障诊断  

Fault diagnosis of shearer rocker bearing based on improved EEMD and HMM

作　　者：王行刚[1] 

机构地区：[1]江苏联合职业技术学院徐州医药分院,江苏徐州221116

出　　处：《工矿自动化》2016年第9期48-51,共4页Journal Of Mine Automation

基　　金：国家高技术研究发展计划(863计划)资助项目(2012AA06A406)

摘　　要：提出一种基于改进集成经验模态分解与隐马尔科夫模型的采煤机摇臂轴承故障诊断方法,利用基于极值点对称延拓和余弦窗函数的改进方法,减少端点效应对分解结果的影响,从而提高了信号分解的精度;然后提取每层本征模态函数的能量熵作为隐马尔科夫模型的输入特征向量,进行故障模式识别。实验结果表明,该方法对轴承故障类型的识别率达90%以上,实现了采煤机摇臂轴承故障的准确诊断。The paper proposed a fault diagnosis method of shearer rocker bearing based on improved EEMD and HMM.The method uses improved extreme points symmetric extension and cosine window function to reduce impact of end effect on decomposition results,so as to improve signal decomposition precision;then extractes energy entropy of each intrinsic mode function as input feature vector of HMM for fault pattern recognition.The experimental results show that bearing fault identification rate of the proposed method is above 90%,which indicates the method achieves accurate fault diagnosis of shearer rocker bearing.

关 键 词：采煤机摇臂 轴承 故障诊断 集成经验模态分解 隐马尔科夫模型 

分 类 号：TD421.6[矿业工程—矿山机电]