Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

作     者：Guangjun Jiang Dezhi Li Ke Feng Yongbo Li Jinde Zheng Qing Ni He Li 

作者机构：School of Mechanical EngineeringInner Mongolia University of TechnologyHohhotInner MongoliaChina Inner Mongolia Key Laboratory of Advanced Manufacturing TechnologyHohhotInner MongoliaChin a 3 Department of Industrial Systems Engineering and ManagementNational University of Singapore117576Singapore School of AeronauticsNorthwestern Polytechnical UniversityXi’an 710068China School of Mechanical EngineeringAnhui University of TechnologyMa’anshan 243002China School of Mechanical and Mechatronic EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia Centre for Marine Technology and Ocean Engineering(CENTEC)Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal 

出 版 物：《Journal of Dynamics, Monitoring and Diagnostics》 (动力学、监测与诊断学报（英文）)

年 卷 期：2023年第2卷第4期

页      面：275-289页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Science and Technology Planning Project of Inner Mongolia of China under contract number 2021GG0346 

主　　题：continuous wavelet transform convolutional capsule network fault diagnosis rolling bearings 

摘      要：Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical *** response to the problem of frequent faults in rolling bearings,this paper designs a rolling bearing fault diagnosis method based on convolutional capsule network(CCN).More specifically,the original vibration signal is converted into a two-dimensional time–frequency image using continuous wavelet transform(CWT),and the feature extraction is performed on the two-dimensional time–frequency image using the convolution layer at the front end of the network,and the extracted features are input into the capsule *** capsule network converts the extracted features into vector neurons,and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault *** different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the *** results show that the CCN has good diagnostic capability under different working conditions,even in the presence of noise and insufficient samples,compared to other *** method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.