机械科学与技术2025，Vol.44Issue(8)：1308-1316,9.DOI:10.13433/j.cnki.1003-8728.20230294
二维多尺度符号样本熵在滚动轴承故障诊断中的应用
Application of Two-dimensional Multi-scale Symbol Sample Entropy in Rolling Bearing Fault Diagnosis
摘要
Abstract
Sample entropy is a nonlinear dynamic analysis method of measuring time sequence complexity,and is also a powerful tool for fault characterization of rolling bearing.However,one-dimensional sample entropy only analyzes the signal information in time domain,while two-dimensional sample entropy can measure the complexity information of the signal in time-frequency distribution.But two-dimensional sample entropy is low efficient and easy to be disturbed by noise.To this end,using symbolic dynamic filtering to eliminate background noise and improve computational efficiency,the paper proposes a new measure of two-dimensional symbolic sample entropy for the complexity of signal time-frequency distributions.At the same time,in order to extract the multi-scale feature of the signal,the two-dimensional symbol sample entropy is extended to multi-scale analysis,and two-dimensional multi-scale symbol sample entropy is proposed.Then a new fault diagnosis method of rolling bearing is proposed based on two-dimensional multi-scale symbol sample entropy and firefly algorithm to optimize support vector machine.Finally,it is compared with the two-dimensional multi-scale sample entropy and two-dimensional multi-scale arrangement by analog signals and measured data analysis,and the results show that the fault diagnosis method is more accurate.关键词
样本熵/二维多尺度样本熵/符号动态滤波/二维多尺度符号样本熵/故障诊断Key words
sample entropy/two-dimensional multi-scale sample entropy/symbol dynamic filtering/two-dimensional multi-scale symbol sample entropy/fault diagnosis分类
信息技术与安全科学引用本文复制引用
孙壮壮,郑近德,童靳于,潘海洋,刘庆运..二维多尺度符号样本熵在滚动轴承故障诊断中的应用[J].机械科学与技术,2025,44(8):1308-1316,9.基金项目
国家自然科学基金项目(51975004)与安徽省自然科学基金项目(2008085QE215) （51975004）
