Hierarchy-based domain adversarial neural network for bearing fault diagnosis under variable working conditions

The bearing fault is one of the primary factors affecting the safe and stable running of mechanical systems. To guarantee the normal and reliable running of the entire equipment, it is crucial to promptly and accurately monitor the operating conditions of bearings. Conventional fault diagnosis methods usually depend upon the assumption that the training and test data are consistently distributed and independent. However, this premise poses challenges to the resolution of fault diagnosis issues for changeable running conditions. To tackle the aforementioned problem, a novel hierarchy-based domain adversarial neural network (H-DANN) is introduced in this paper. For the proposed H-DANN model, it is mainly constructed based on the DANN. The domain discriminator enables the feature extractor to abstract domain-independent features and allows classifier transfer across different operating environments. Furthermore, to extract rich discriminative features, a hierarchy-based feature extractor is proposed based on a novel feature pyramid network (FPN) modified by the CNN-BiLSTM network. Finally, the results of two bearing datasets indicate that the H-DANN model is adept at precisely recognizing bearing fault categories under different running environments, outperforming some state-of-the-art models.