Open AccessRolling Bearing Fault Diagnosis and Prediction Based on VMD-CWT and MobileNet
Author(s) -
Jing Zhu,
Aidong Deng,
Shuo Xue,
Xue Ding,
Shun Zhang
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110305
Subject(s) - bearing (navigation) , computer science , feature extraction , fault (geology) , artificial intelligence , convergence (economics) , deep learning , pattern recognition (psychology) , wavelet , wavelet transform , artificial neural network , process (computing) , transfer of learning , geology , seismology , economics , economic growth , operating system
When deep learning is used for rolling bearing fault diagnosis, there are problems of high model complexity, time-consuming, and large memory. In order to solve this problem. This paper presents an intelligent diagnosis method of rolling bearings based on VMD-CWT feature extraction and MobileNet, VMD is used to extract the signal features, and then wavelet transform is used to extract the timefrequency features. After the image is enhanced, the MobileNet network is trained. In order to accelerate the convergence speed, this paper adds transfer learning in the network training process, and migrates the weights of the first several layers pretrained to the corresponding network. Experimental results based on bearing fault data sets show that after adopting VMD-CWT, the accuracy of mobilenet increased from 68.7% to 94%, and its network parameters were reduced by an order of magnitude compared with CNN.