Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

In industrial scenarios, the source-domain (SD) data typically encompasses condition monitoring (CM) data from all machines within a workshop or factory setting, while the target-domain (TD) data may only include CM data from one or a small number of machines. The intelligent diagnostic method based on partial domain adaptation (PDA) represents a powerful tool for aligning features between SD and TD data within partial categories. However, existing PDA techniques can only align either the marginal or conditional distributions (CDs) between SD and TD data within the shared label space, but not both simultaneously. To overcome this limitation, our study introduces a dual structural consistent PDA network. This network leverages the vision transformer (ViT) as its foundation, ensuring effective extraction of distinguishable features from both SD and TD data. A weight balance mechanism is integrated into the partial adversarial training (PAT) process, facilitating marginal distribution alignment (MDA) between SD and TD data within the shared label space. Additionally, a knowledge distillation (KD)-based approach is employed for CD alignment (CDA) across the two structural consistent networks (SCNs), ensuring consistency in predictions for TD data. The effectiveness of our proposed method is demonstrated through its application on two sets of experimental faulty data, confirming its ability to provide a feature distribution that is not affected by domain changes but is discriminative for different classes when dealing with PDA tasks.