Deep learning for bearing fault diagnosis under different working loads and non-fault location point
Intelligent fault diagnosis using deep learning has achieved much success in recent years. Using deep learning method to diagnose bearing fault requires designing an appropriate neural network model and then train with a massive data. On the one hand, up to now, a variety of neural network structure...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-03-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/1461348419889511 |
| Summary: | Intelligent fault diagnosis using deep learning has achieved much success in recent years. Using deep learning method to diagnose bearing fault requires designing an appropriate neural network model and then train with a massive data. On the one hand, up to now, a variety of neural network structures have been proposed for different diagnostic tasks, but there is a lack of research of unified structure. On the other hand, the fault data of the training neural network are collected from the fault location point, which is quite different from the actual data, because the sensor cannot be located at the fault location point accurately. This paper attempts to design a unified neural network structure based on Resnet and improve the generalization performance by using transfer learning techniques. The effectiveness of the proposed method in this paper is verified using experiment under different working loads and non-fault location point. |
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| ISSN: | 1461-3484 2048-4046 |