基于阶次跟踪最大相关峭度反褶积的滚动轴承早期故障诊断  

Early Fault Diagnosis for Rolling Bearing Based on Order Tracking Maximum Correlation Kurtosis Deconvolution

作　　者：任学平[1] 张玉皓[1] 辛向志 庞震[1] 

机构地区：[1]内蒙古科技大学机械工程学院,内蒙古包头014010

出　　处：《机械设计与制造》2016年第2期161-164,共4页Machinery Design & Manufacture

基　　金：内蒙古自治区自然科学基金项目(2012MS0717)

摘　　要：针对滚动轴承早期故障冲击信号受到现场噪声的干扰,难以提取周期冲击成分的问题,以及非平稳转速下对故障信号直接进行傅里叶分析会出现频率混叠,无法确定故障特征频率的问题。提出基于角域最大相关峭度反褶积的滚动轴承故障诊断方法。首先对时域非平稳故障信号进行计算阶次跟踪转换为角域内的平稳信号;然后用最大相关峭度反褶积对故障信号进行处理,提取信号中的周期冲击成分。通过对仿真和实验数据的分析,验证了角域最大相关峭度反褶积方法的有效性。For early fault impact signal of the rolling bearing interfered by noise,it is difficult to extract the periodic impulse component problems,and under the condition of non-stationary speed,directly on the fault signal carries out fourier analysis which will appear the frequency aliasing phenomenon,and can not determine the fault feature frequency problem. It puts forward the method of rolling bearing fault diagnosis based on angle domain maximum correlation kurtosis deconvolution. First of all,time domain non-stationary fault signal calculates order tracking into angle domain stationary signal; and then uses the maximum correlation kurtosis deconvolution to process the fault signal,and to extract periodic impulse component from the original signal. Through the analysis of the simulation and the experimental data,verifies the validity of angle domain maximum correlation kurtosis deconvolution method.

关 键 词：滚动轴承 早期故障诊断 阶次跟踪 最大相关峭度反褶积 

分 类 号：TH16[机械工程—机械制造及自动化]