Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain
This addresses fault diagnosis in rotating machinery for industrial maintenance, but it is incremental as it builds on existing domain adaptation techniques.
The study tackled compound fault diagnosis in motors with partially labeled target data by developing a multi-output classification framework that independently classifies each fault's severity, achieving superior performance over baselines in macro F1 score across six domain adaptation cases.
This study presents a novel multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, addressing challenges posed by partially labeled (PL) target domain dataset and coexisting faults in rotating machinery. Unlike conventional multi-class classification (MCC) approaches, the MOC framework independently classifies the severity of each fault, enhancing diagnostic accuracy. By integrating multi-kernel maximum mean discrepancy loss (MKMMD) and entropy minimization loss (EM), the proposed method improves feature transferability between source and target domains, while frequency layer normalization (FLN) effectively handles stationary vibration signals by leveraging mechanical characteristics. Experimental evaluations across six domain adaptation cases, encompassing partially labeled (PL) scenarios, demonstrate the superior performance of the MOC approach over baseline methods in terms of macro F1 score.