Label correction of inter-turn short-circuit faults in PMSMs based on high-frequency signal injection
Inter-turn short-circuit faults are a common failure mode in permanent magnet synchronous motors, and their early diagnosis is essential for ensuring safe and reliable operation. In practical applications, however, training data are often contaminated with noisy labels, while incipient fault features are weak and exhibit limited inter-class separability, which further aggravates label misassignment and mixing. To enhance data usability and label reliability, this paper proposes a diagnostic framework integrating high-frequency signal injection with unsupervised learning. High-frequency signal injection is used to strengthen fault-related current features, followed by autoencoder-based feature extraction, UMAP dimensionality reduction, and HDBSCAN clustering to group samples, after which a majority voting strategy is applied for sample relabeling. Experimental results demonstrate that the proposed method can effectively improve noisy-label correction performance even under high noise ratios and low fault severity, thereby providing reliable data for subsequent fault diagnosis.