Unsupervised Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Guided by Density-Based Clustering and Deep Learning

The operational efficiency of a permanent magnet synchronous motor (PMSM) is intrinsically linked to its stability, making the accurate diagnosis of common faults such as interturn short circuits (ITSCs) crucial. ITSC can cause significant damage to the PMSM and its system. Traditional data-driven fault diagnosis methods often require a large amount of expert-labeled data to ensure accuracy, which is challenging to obtain in actual operations. To address this, this article introduces an unsupervised learning (UL) method for ITSC fault diagnosis that extracts inherent data patterns from unlabeled samples. The approach begins with a unique data processing method and the fast Fourier transform to handle the original three-phase current data, thereby reducing data complexity. Following this, an unsupervised framework, utilizing noise-aware density-based clustering with auto-tuning (NADCAT), is constructed, in which NADCAT provides pseudo-labels for deep neural networks, enabling precise mapping of features to ITSC faults. A PMSM experiment platform was established to validate the method proposed in this article. The pseudo-labels obtained by the proposed NADCAT achieved a 92.8% effective rate, and the diagnostic model attained an accuracy of 97.63%. These results demonstrate the effectiveness of the method proposed in diagnosing ITSC faults of the PMSM.