Mixed Attention Network for Source-Free Domain Adaptation in Bearing Fault Diagnosis
Polytropic working conditions are prioritized by most intelligent adaptive methods. But the privacy and the inaccessibility of the source data during the transfer learning process are not considered. Moreover, the attention weights for each time point and neural network channel in the fault signals...
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| Veröffentlicht in: | IEEE access 2024, Vol.12, p.93771-93780 |
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| creator | Liu, Yijiao Yuan, Qiufan Sun, Kang Huo, Mingying Qi, Naiming |
| description | Polytropic working conditions are prioritized by most intelligent adaptive methods. But the privacy and the inaccessibility of the source data during the transfer learning process are not considered. Moreover, the attention weights for each time point and neural network channel in the fault signals are also not addressed. Intending to deal with the problems above, we put forward a mixed attention network for source-free domain adaptation in bearing fault diagnosis work. We fully utilize the only-once source fault information to generate a source model with strong anomaly detection capabilities by our mixed attention network. Mixed attention network achieved an average accuracy of over 93% in both two datasets and achieved the highest accuracy in all tasks of the ablation experiment. |
| doi_str_mv | 10.1109/ACCESS.2024.3424476 |
| format | Article |
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| subjects | Ablation Adaptation Anomalies Convolutional neural network Convolutional neural networks Deep learning Fault detection Fault diagnosis Feature extraction Kernel Knowledge transfer multi-layer neural network Neural networks Prognostics and health management Task analysis time-domain analysis Transfer learning unsupervised learning |
| title | Mixed Attention Network for Source-Free Domain Adaptation in Bearing Fault Diagnosis |
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