机械科学与技术2025，Vol.44Issue(7)：1151-1158,8.DOI:10.13433/j.cnki.1003-8728.20230276
加权精细复合多尺度散布熵与改进贝叶斯网络结合的轴承故障诊断
Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks
摘要
Abstract
In order to extract the fault features of bearing vibration signals more accurately,weighted fine composite multi-scale spread entropy(wRCMDE)was introduced into bearing fault feature extraction.On this basis,a rolling bearing fault diagnosis method based on wRCMDE and improved Bayesian network was proposed.By calculating wRCMDE of different fault vibration signals and selecting multiple wRCMDE values at appropriate scale as feature vectors,feature samples were formed and input into the Bayesian network optimized by the improved firefly algorithm for fault classification and recognition.Through the analysis of experimental data,the proposed method is compared with the fault feature extraction method based on multiscale dispersal entropy and refined composite multiscale dispersal entropy.Experimental results show that this method can identify the fault types of rolling bearings more accurately,and the recognition rate is higher.关键词
加权精细复合多尺度散布熵/萤火虫算法/贝叶斯网络/故障诊断Key words
weighted refined composite multiscale dispersion entropy/firefly algorithm/Bayesian network/fault diagnosis分类
机械制造引用本文复制引用
仝兆景,孟令强,唐晋豪,吴鹏..加权精细复合多尺度散布熵与改进贝叶斯网络结合的轴承故障诊断[J].机械科学与技术,2025,44(7):1151-1158,8.基金项目
国家自然科学基金项目(U1504623)、河南省软科学研究计划(252400410717)、河南省高等教育教学改革研究与实践项目(研究生教育类)(2023SJGLX144Y)及河南理工大学研究生教改项目(2023YJ20) （U1504623）
