现代电子技术2024，Vol.47Issue(1)：171-176,6.DOI:10.16652/j.issn.1004-373x.2024.01.030
基于混合式特征选择的滚动轴承故障诊断方法
Rolling bearing fault diagnosis based on hybrid feature selection
摘要
Abstract
A hybrid feature selection method is proposed to reduce the dimensionality of the rolling bearing fault feature sets and improve the fault diagnostic accuracy.The implementation of the method consists of two stages.In the former stage,the original feature sets are pre-ranked by the Fisher score(FS)method,the features are sorted in descending order according to their FSs,the inflection point of the score curve is used to determine the range of the pre-selected subsets,and the irrelevant features are removed from the original feature sets.In the later stage,the genetic algorithm(GA)is embedded into the stage of Wrapper,and the recognition accuracy of the classifier is used as the evaluation criterion to remove redundant features from the pre-selected subset features,so as to determine the optimal subset.The experiment proves that the proposed method can be effectively used for the diagnosis of different fault types and different fault degrees of rolling bearings,and the recognition accuracy of the optimal subset is improved while only the key features are retained.关键词
滚动轴承/混合式特征选择/费舍尔分值/遗传算法/冗余特征/故障诊断Key words
rolling bearing/hybrid feature selection/FS/GA/redundant feature/fault diagnosis分类
信息技术与安全科学引用本文复制引用
司宇,章翔峰,张罡铭,姜宏..基于混合式特征选择的滚动轴承故障诊断方法[J].现代电子技术,2024,47(1):171-176,6.基金项目
国家自然科学基金项目(51865054) （51865054）
国家自然科学基金项目(52265016) （52265016）
