A Trend Domain Adaptation Approach With Dynamic Decision for Fault Diagnosis of Rotating Machinery Equipment

Nowadays, transfer learning (TL) is widely used in fault diagnosis of machinery, which greatly broadens its application in scenarios with variable operating conditions. However, existing TL-based fault diagnosis methods usually emphasize on the research of domain adaptation (DA) mechanisms, and neglect the impact of the expressiveness of the classifier on DA. To overcome this limitation of existing DA-based diagnosis methods, the dynamic softmax with angular margin penalty is designed to dynamically adjust the expressiveness of the embeddings learned by the encoder network. In this way, the diagnosis network can learn more representative features, which improves the robustness of the network on the target data. Furthermore, a trend block is designed to learn trend features in the vibration signal, so that the fault features learned by the feature extractor are more abundant. Comprehensive experiments on real and public datasets show that our approach outperforms other well-established cross-domain fault diagnosis algorithms.