Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor

This work introduces a multi-output classification (MOC) fra mework designed for domain adaptation in fault diagnosis, particularly under partially la beled (PL) target domain scenarios and comp ound fault conditions in rotating machinery. Unlike traditional multi-class classification (MCC) methods that treat each fault combination as a distinct class, the proposed approach independently estimates the severity of each fau lt type, improving both interpretability and diagnostic accuracy. The model incorporates multi-ke rnel maximum mean discrepancy (MK-MMD) and entropy minimization (EM) losses to facilitate feature tran sfer from the source to the target domain. In addition, frequency layer normalization (FLN) is applied to preserve structural properties in the frequen cy domain, which are strongly influenced by system dynamics and are often stationary with respect to changes in rpm. Evaluations across six domain ad aptation cases with PL data demonstrate that MOC outperforms baselin e models in macro F1 score. Moreover, MOC consistently achieves better classification performance for individual fault types, and FLN shows superior adaptability compared to other normalization techniques.