Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Guangjun Jiang; Dezhi Li; Ke Feng; Yongbo Li; Jinde Zheng; Qing Ni; He Li

doi:10.37965/jdmd.2023.260

Authors

Guangjun Jiang Inner Mongolia University of Technology & Inner Mongolia Key Laboratory of Advanced Manufacturing Technology,Inner Mongolia, China
Dezhi Li Inner Mongolia University of Technology & Inner Mongolia Key Laboratory of Advanced Manufacturing Technology,Inner Mongolia, China https://orcid.org/0009-0001-4890-1241
Ke Feng Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore https://orcid.org/0000-0003-2338-5161
Yongbo Li School of Aeronautics, Northwestern Polytechnical University, Xi’an 710068, China
Jinde Zheng School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243002, PR China
Qing Ni School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Australia
He Li Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal https://orcid.org/0000-0001-6429-9097

DOI:

https://doi.org/10.37965/jdmd.2023.260

Keywords:

Fault diagnosis; Convolutional Capsule Network; Continuous wavelet transform; Rolling bearings;

Abstract

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment. In 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 network. The 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 diagnosis. Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method. The results show that the convolutional capsule network has good diagnostic capability under different working conditions, even in the presence of noise and insufficient samples, compared to other models. This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.

Conflict of Interest Statement

The authors declare no conflicts of interest.