中国机械工程2017，Vol.28Issue(5)：532-536,543,6.DOI:10.3969/j.issn.1004-132X.2017.05.005
基于双树复小波和深度信念网络的轴承故障诊断
Bearing Fault Diagnosis Based on DTCWT and DBN
摘要
Abstract
Based on DTCWT and DBN,a new method of bearing fault diagnosis was proposed.Ex-periments on bearing vibration signals decomposition show that the signals may be well decomposed into different frequency bands by DTCWT.Then,power entropy of different frequency bands were taken as the fault features and input to the model for classification and the traditional classifiers were taken as the comparison.Results show that the method may identify different fault types accurately, which expands the applications of DBN.关键词
双树复小波/深度信念网络/受限波尔兹曼机/故障诊断Key words
dual-tree complex wavelet transform (DTCWT)/deep belief network (DBN)/restricted Boltzmann machine (RBM)/fault diagnosis分类
信息技术与安全科学引用本文复制引用
张淑清,胡永涛,姜安琦,李军锋,宿新爽,姜万录..基于双树复小波和深度信念网络的轴承故障诊断[J].中国机械工程,2017,28(5):532-536,543,6.基金项目
国家自然科学基金资助项目(51475405,61077071) （51475405,61077071）
河北省自然科学基金资助项目(F2015203413,F2016203496,F2015203392) （F2015203413,F2016203496,F2015203392）
