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About 07961149transfer Learning
This document presents a transfer learning approach using neural networks for bearing fault diagnosis in changing working conditions. The approach first learns characteristics from source data collected in one working condition. It then adjusts the parameters of neural networks and alters the network structure based on changes in data distribution. Some parameters are transferred from the source task to the target task in another working condition. Finally, the model is trained with a small amount of target data to improve classification accuracy compared to conventional neural networks when limited target data is available. The approach was validated using bearing vibration data from the Case Western Reserve University.
Detailed Information
| Author: | ['judarangoca'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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