Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

Abstract

With the rapid development of industrial applications using permanent magnet synchronous drives (PMSM) and the Internet of Things, the demand for using robust fault diagnosis methods working in noisy conditions has increased significantly. The current data-driven methods depend mainly on deep learning (DL) models due to the effectiveness of automated feature extraction. However, these models have shallow depths compared with benchmark convolution neural networks, limiting their accuracy in final predictions, and they are established based on the hypothesis that the measured data is noiseless. Despite this, electric machinery is subjected to various noise sources that interfere with measurements during operation. This article proposes a new scheme combining a transfer-learned pre-trained residual neural network (ResNet) and supervised machine learning (S-ML) to enhance the performance of deep learning models in noisy industrial environments. The effectiveness of the proposed scheme is validated using an in-house setup of a PMSM drive with demagnetization and intern-short circuit faults at variant operating conditions. The results show that the proposed method significantly reduced the computational burden by tenfold on average while improving the average accuracy to 96.84 % across all the datasets compared with other DL and S-ML methods, with high robustness in noisy working conditions.INDEX TERMS Deep transfer learning, data-driven fault diagnosis, hybrid learning, permanent magnet synchronous motors (PMSMs), pre-trained deep models, wavelet analysis.

Linear Matrix Inequality-Based Robust Model Predictive Speed Control for a Permanent Magnetic Synchronous Motor with a Disturbance Observer

“…Through the optimization problem in (28), the decision matrices P, P o , and Y are calculated, and as a result, the gain matrices of the disturbance observer and state observer are determined as follows. minimize P,P o ,Y α subject to (23) and (27).…”

“…In addition, we propose a gain-tuning procedure to find the optimal gains of observer and controller. This proposed method is expected to be suitable for stable operation in environments where the PMSM's parameters change due to long-term operation or torque changes rapidly [26,27].…”

Unsupervised anomaly detection of permanent magnet offshore wind generators through electrical and electromagnetic measurements

“…However, despite their increased reliability, PMSGs are not completely immune to faults and have their own challenges, particularly in harsh offshore environments. Consequently, reliable condition monitoring and early fault detection are essential to minimize production loss and prevent unexpected downtime in these machines (Nejad et al, 2022;Huang et al, 2023;Yang, 2009;Mahmoud et al, 2024). PM machines, in general, are susceptible to several types of faults that can impact their functionality (Choi et al, 2018;Kudelina et al, 2021).…”