Heterogeneous dual-stream contrastive adversarial network for cross-domain mechanical fault diagnosis

Abstract

In the cross-machine fault diagnosis task of bearings for rotating machinery, inherent physical differences among different mechanical devices cause severe domain distribution shift of fault features. Consequently, the diagnostic model trained on the source machine suffers a dramatic decline in generalization performance when applied to diagnosis tasks of target machines, making it difficult to meet the requirements of practical industrial diagnosis. To address this issue, this paper proposes a heterogeneous dual-stream contrastive adversarial network (HDCAN). Firstly, one-dimensional (1D) vibration signals are converted into two-dimensional images via the symmetric dot pattern, constructing a signal-image multimodal input system. Secondly, a parallel dual-stream network architecture is designed, and a cross-modal contrastive learning strategy is introduced simultaneously to effectively eliminate the negative transfer effect between heterogeneous modalities and realize the pre-alignment of multimodal features. On this basis, the domain adversarial learning mechanism is integrated to further extract generalized features with both fault discriminability and domain invariance, so as to improve the cross-machine adaptation capability of the model. To verify the effectiveness of the proposed method, experiments are conducted on six cross-machine diagnosis tasks from three public bearing fault datasets. The experimental results show that the average diagnostic accuracy of the HDCAN model reaches 92.10%, which significantly outperforms the current mainstream domain adaptive fault diagnosis methods. The experimental results fully demonstrate that the proposed HDCAN can effectively alleviate the distribution shift problem of cross-machine fault data and possesses great application potential in industrial practical scenarios.