Partial Domain Adaptation for Intelligent Machinery Fault Diagnosis

Accurate gearbox fault diagnosis across different operating conditions plays an important role in prognostics and health management. In real industrial scenarios, a common challenge arises when the source domain contains multiple fault classes, while the target domain includes only healthy samples during training. To address this issue, this study proposes a unified industrial fault diagnosis framework designed to handle the partial domain adaptation problem. Specifically, the overall framework involves: a unified data processing pipeline, a robust deep learning architecture for accurate fault classification, and integration of maximum mean discrepancy loss to align feature distributions between source and target domains. Experimental results demonstrate that our proposed partial domain adaptation-based deep learning model significantly outperforms benchmark models, achieving accuracy improvements exceeding 20% across multiple domain adaptation tasks. This study provides a practical solution for intelligent gearbox diagnosis under domain shift constraints.