机械科学与技术2026，Vol.45Issue(3)：392-403,12.DOI:10.13433/j.cnki.1003-8728.20240068
CSC-RCMSDE结合MRVM的轴承声振融合故障诊断算法
Acoustic-vibration Fused Bearing Fault Diagnosis Method Combining CSC-RCMSDE and MRVM
摘要
Abstract
The existing rolling bearing intelligent diagnosis algorithm is mainly based on a single vibration acceleration signal,and the feature extraction process requires human selection,which cannot guarantee the stability of feature quality.In view of the above problems,a multiclass relevance vector machine(MRVM)bearing fault diagnosis method is proposed using the multicore parameter optimization of acoustic and vibration fusion features of bearing faults.Firstly,the concept of combined silhouette coefficient(CSC)is proposed;secondly,the CSC is utilized as an adaptation function to adaptively determine the symbol number ε and the embedding dimension m of the refined composite multiscale symbolic dynamic entropy(RCMSDE).Finally,the MRVM with multicore parameter optimization is used to realize the fault diagnosis based on bearing acoustic vibration signal fusion features.The effectiveness of CSC for quantifying the class distinctiveness among sample feature vectors is verified using the public dataset of Case Western Reserve University;the effectiveness of the proposed CSC-RCMSDE-MRVM method is verified by the data from the test bed of traction motor bearings of a high-speed train.The accuracy of fault identification reaches 99.91%,which is higher than that of the traditional RCMSE-MRVM method of 99.39%,the RCMFE-MRVM method of 99.55%,and single-channel CSC-RCMSDE-MRVM method(96.01%for vibration signal and 98.95%for acoustic signal).关键词
轴承故障诊断/精细复合多尺度符号动态熵/多分类相关向量机/综合轮廓系数/声振融合Key words
bearing fault diagnosis/refined composite multiscale symbolic dynamic entropy/multiclass relevance vector machine/combined silhouette coefficient/acoustic-vibration fusion分类
机械制造引用本文复制引用
杨岗,徐五一,邓琴,成雷,王志旗..CSC-RCMSDE结合MRVM的轴承声振融合故障诊断算法[J].机械科学与技术,2026,45(3):392-403,12.基金项目
国家重点研发计划(2020YFB1200300ZL)与四川省重点研发项目(2023YFG0063) （2020YFB1200300ZL）
