Co-training partial domain adaptation networks for industrial Fault Diagnosis
This addresses the partial domain adaptation challenge for industrial fault diagnosis, offering a novel framework with practical usability, though it appears incremental in its approach.
The paper tackles the partial domain adaptation problem in industrial fault diagnosis by proposing Interactive Residual Domain Adaptation Networks (IRDAN), which uses domain-wise models with residual blocks and interactive learning to mitigate domain shift, achieving superior performance in experiments.
The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to mitigate the ADP problem. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN.