提出新框架提升工业故障诊断中的部分域自适应效果
Co-training partial domain adaptation networks for industrial Fault Diagnosis
- 用领域专用模型+残差适配块缓解领域偏移问题
- 通过交互式学习避免模块间干扰，提升训练稳定性
- 配备可靠停止准则，适合实际工业场景部署
部分域自适应（PDA）是工业故障诊断中的常见挑战。受传统分类任务中无需考虑此类问题的启发，本文提出一种名为交互式残差域适应网络（IRDAN）的新PDA框架。该框架为每个领域设计专属模型，并引入残差域适应（RDA）模块以缓解域偏移问题。同时，通过交互式学习策略实现模块的顺序训练，避免交叉干扰。此外，构建了可靠的停止准则，确保模型在真实应用中的可用性。实验表明，所提方法在多个数据集上均优于现有基准。
原文摘要 · Abstract (English)
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.
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