针对电机复合故障诊断，提出多输出分类框架提升准确性与可解释性。
Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor
- 将每种故障独立建模为输出，替代传统分类方式，增强可解释性。
- 在六组部分标签数据上，宏平均F1得分显著优于基线模型。
- 引入频域归一化，有效保留转速变化下的结构特征，适合工程部署。
本文提出一种用于旋转机械部分标签目标域场景下故障诊断的多输出分类（MOC）框架，解决复合故障问题。不同于将每个故障组合视为独立类别的传统多分类方法，该框架分别估计各类故障的严重程度，提升了诊断可解释性与准确性。模型结合多核最大均值差异（MK-MMD）与熵最小化损失，实现源域到目标域的特征迁移。同时引入频率层归一化（FLN），保留受系统动态影响、随转速变化稳定的频域结构特性。在六个领域自适应案例中，MOC在宏平均F1得分上优于基准模型，且对单个故障类型的分类性能更优；相比其他归一化方法，FLN展现出更强适应性。
原文摘要 · Abstract (English)
This work introduces a multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, particularly under partially labeled (PL) target domain scenarios and compound 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 fault type, improving both interpretability and diagnostic accuracy. The model incorporates multi-kernel maximum mean discrepancy (MK-MMD) and entropy minimization (EM) losses to facilitate feature transfer from the source to the target domain. In addition, frequency layer normalization (FLN) is applied to preserve structural properties in the frequency domain, which are strongly influenced by system dynamics and are often stationary with respect to changes in rpm. Evaluations across six domain adaptation cases with PL data demonstrate that MOC outperforms baseline 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.
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