机械科学与技术2025，Vol.44Issue(10)：1774-1784,11.DOI:10.13433/j.cnki.1003-8728.20230344
MMI-SSVP的机床轴承故障特征提取应用研究
Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction
摘要
Abstract
The reconstruction based on the singular value decomposition can effectively separate and suppress random noise components in monitoring signals,but its performance is limited by the construction of trajectory matrixes and effective component evaluation and selection.To solve this problem,an adaptive sum singular value pair(SSVP)optimization framework based on the min mutual information(MMI)is proposed and applied to the feature extraction of machine tool bearing fault signals.Firstly,singular value and singular value vector are calculated with the anti-angular average method,and singular value pairs(SVP)are obtained with the characterization ability of the SVP of sub-signal energy.Then,the optimal reconstructed components are obtained adaptively based on the MMI index,avoiding over-noise reduction or under-noise reduction.Meanwhile,the singular value ratio indexes of MMI are combined to determine the number of optimal decomposition dimensions of the Hankel matrix.Finally,the validity of the MMI-SSVP method is verified with the data of the faulty bearings of a spindle and the feeding system of a machining center in an industrial site respectively.关键词
奇异值分解/累加奇异值子对/最小互信息/奇异值比/特征提取Key words
singular value decomposition/sum singular value pair/min mutual information/singular value ratio/feature extraction分类
机械工程引用本文复制引用
康怡,刘韬,施庆华,王振亚..MMI-SSVP的机床轴承故障特征提取应用研究[J].机械科学与技术,2025,44(10):1774-1784,11.基金项目
国家自然科学基金项目(52065030)与云南省科技厅重大专项(202202AC080008) （52065030）
