Mixed Attention Network for Source-Free Domain Adaptation in Bearing Fault Diagnosis

Abstract

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.

Keywords

• Convolutional neural network
• deep learning
• fault detection
• fault diagnosis
• knowledge transfer
• multi-layer neural network