一种煤矿机械轴承故障诊断方法  

A fault diagnosis method for coal mine machinery bearing

作　　者：徐青青[1] 赵海芳[1] 李守军[1] XU Qingqing;ZHAO Haifang;LI Shoujun(School of Mechanical Engineering,Suqian College,Suqian 223800,China)

机构地区：[1]宿迁学院机电工程学院

出　　处：《工矿自动化》2019年第10期80-85,90,共7页Journal Of Mine Automation

基　　金：江苏省高等学校自然科学研究资助项目(19KJD460008,19KJB440002);江苏省高校品牌专业建设工程资助项目(PPZY2015C252)

摘　　要：针对现有煤矿机械轴承故障自适应诊断方法易受高频噪声和间断噪声干扰而导致原始信号分解和特征提取精度较低的问题,提出了一种基于改进局部均值分解的煤矿机械轴承故障诊断方法。该方法在局部均值分解方法的自适应分解部分采用噪声辅助分解方法,将高斯白噪声加入原始信号,然后进行局部均值分解,以抑制高频噪声及间断噪声对信号分解的影响;在特征参数提取部分对乘积函数分量进行Hilbert变换,然后进行特征参数提取,以实现在全部取值范围内提取特征参数。仿真及测试结果表明,该方法对轴承故障信号分解和特征参数提取的效果较好,对轴承内外圈故障诊断的准确性较高。Aiming at the problem that existing adaptive diagnosis methods of coal mine machinery bearing fault were susceptible to the interference of high frequency noise and intermittent noise,which led to the low accuracy of original signal decomposition and feature extraction,a fault diagnosis method for coal mine machinery bearing was proposed which was based on modified local mean decomposition(MLMD).The method adopts adjuvant noise decomposition method in self-adaptive decomposition part of local mean decomposition(LMD) method,namely adding Gaussian white noise to original signal firstly and then carrying out LMD,so as to restrain influence of high-frequency and intermittent noise on signal decomposition.In feature parameter extraction part,MLMD method does Hilbert transformation for product function components,then extracts feature parameters,so as to realize feature parameter extraction in whole value range.The simulation and test results show that MLMD method has good effect on decomposition and feature parameter extraction of bearing fault signal and high diagnosis accuracy of inner and outer ring fault of bearing.

关 键 词：煤矿机械 轴承故障 故障诊断 局部均值分解 自适应分解 特征参数提取 噪声辅助分解 

分 类 号：TD67[矿业工程—矿山机电]