机械科学与技术2024，Vol.43Issue(6)：967-974,8.DOI:10.13433/j.cnki.1003-8728.20230065
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
摘要
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分类
机械制造引用本文复制引用
刘沛,彭珍瑞,何泽人..ITD结合参数优化MOMEDA的滚动轴承故障特征提取[J].机械科学与技术,2024,43(6):967-974,8.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)及兰州市人才创新创业项目(2017-RC-66). （20JR10RA209）
