针对部分标签故障诊断，提出多输出分类新方法。
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain
- 独立判断每种故障严重程度，提升诊断精度。
- 在六组迁移场景中，宏平均F1分数显著优于基线。
- 适合处理设备故障共现与标签不全的工业场景。
本研究提出一种新型多输出分类（MOC）框架，用于解决旋转机械中部分标签目标域数据集及共存故障带来的故障诊断挑战。与传统多类分类（MCC）不同，MOC框架对每种故障的严重程度进行独立分类，从而提升诊断准确性。通过融合多核最大均值差异损失（MKMMD）和熵最小化损失（EM），该方法增强源域与目标域间特征可迁移性；同时，频率层归一化（FLN）利用机械特性有效处理平稳振动信号。在六组领域自适应案例中的实验评估表明，该方法在部分标签（PL）场景下，相比基线模型在宏平均F1分数上表现更优。
原文摘要 · Abstract (English)
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.
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