ITD结合参数优化MOMEDA的滚动轴承故障特征提取
Fault Feature Extraction of Rolling Bearing Combining ITD and Parameter Optimized MOMEDA
刘沛 1彭珍瑞 1何泽人1
作者信息
- 1. 兰州交通大学机电工程学院,兰州 730070
- 折叠
摘要
针对固有时间尺度分解(Intrinsic time scale decomposition,ITD)方法在强背景噪声影响下难以提取轴承故障特征的问题,提出了一种ITD与参数优化的多点最优最小熵解卷积(Multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)相结合的滚动轴承故障特征提取方法.首先根据包络谱峰值因子最大原则提取包含丰富故障信息的ITD分量,其次对该分量进行MOMEDA降噪处理.对影响MOMEDA滤波效果的两个参数——故障周期T与滤波器长度L分别以多点峭度和平方包络谱的基尼指数进行优化,最后进行包络谱分析提取故障特征频率.通过仿真信号与实测信号分析表明该方法能在强噪声干扰下有效提取故障特征.
Abstract
Aiming at the problemthat the intrinsic time scale decomposition(ITD)method is difficult to extract bearing fault features under the influence of strong background noise,a new fault features extraction method for rolling bearings combining ITD and parameter optimized multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)is proposed.First,the ITD component containing rich fault information is extractedfrom fault signals according to the principle of maximum crest factor of envelope spectrum.Then,the MOMEDA noise reduction process is performed on the decomposedcomponent.The two parameters-fault period T and filter length L that affect the filtering effect of MOMEDA,are optimized with multi-point kurtosis and Gini index of square envelope spectrum respectively.Finally,envelope spectrum analysis is performed to extract fault characteristic frequencies.The analysis of the simulated signal and the measured signal shows that the new method can effectively extract the fault features of rolling bearings under the strong noise interference.
关键词
固有时间尺度分解/多点最优最小熵解卷积/滚动轴承/包络谱峰值因子/基尼指数Key words
intrinsic time scale decomposition/multipoint optimal minimum entropy deconvolution adjusted/rolling bearing/crest factor of envelope spectrum/Gini index引用本文复制引用
基金项目
甘肃省自然科学基金重点项目(20JR10RA209)
甘肃省高校协同创新团队项目(2018C-12)
兰州市人才创新创业项目(2017-RC-66)
出版年
2024
国家科技期刊平台
NSTL
万方数据