Fault prediction of computer image recognition based on convolutional neural network
1
School of Information Engineering, Zhoukou Polytechnic Vocational College, Zhoukou 466001, China
Submission date: 2024-11-27
Final revision date: 2025-07-17
Acceptance date: 2025-07-29
Online publication date: 2025-08-12
Publication date: 2025-08-12
Corresponding author
Weiwei Tong
School of Information Engineering, Zhoukou Polytechnic Vocational College, Zhoukou 466001
School of Information Engineering, Zhoukou Polytechnic Vocational College, Zhoukou 466001
KEYWORDS
TOPICS
ABSTRACT
In order to solve the problem that traditional bearing fault diagnosis methods need a lot of professional knowledge, this paper proposes a fault prediction method of computer image recognition based on convolutional neural network. First of all, the concept V3 model is used as the pre training model, and the concept V3 model training method combining deep learning and transfer learning is designed; Then, the cross entropy is used as the loss function to evaluate the effect of model training, and the method and steps of fault diagnosis are given. The validity of the method is verified by the vibration data of bearings in normal and different fault states; Finally, the principal component analysis method is used to analyze the clustering effect of the characteristic parameters extracted by the inception V3 model on different fault modes. By comparing and analyzing the training times and training time of the inception V3 model with and without the transfer learning, the improvement effect of the transfer learning method on the training speed of the model is verified.
FUNDING
This research received no external funding.
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| eISSN: | 2449-5220 |
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