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About Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning
This paper proposes a novel deep learning framework for highly accurate machine fault diagnosis using transfer learning. The framework first converts sensor time series data to time-frequency images using wavelet transformations. It then extracts lower-level features from these images using a pre-trained network. The labeled images are used to fine-tune the higher levels of the neural network. This transfer learning approach enables faster training and more accurate fault diagnosis compared to existing methods. The framework is tested on three machine fault datasets, achieving near 100% accuracy on most and significantly improving accuracy from 94.8% to 99.64% on one dataset.
Detailed Information
| Author: | ['Jaime'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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