Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.

If the circular holes of an engine cylinder head are distorted, cracked, defective, etc., the normal running of the equipment will be affected. For detecting these faults with high accuracy, this paper proposes a detection method based on feature point matching, which can reduce the detection error caused by distortion and light interference. First, the effective and robust feature vectors of pixels are extracted based on improved sparse Haar-like features. Then we calculate the similarity and find the most similar matching point from the image. In order to improve the robustness to the illumination, this paper uses the method based on image similarity to map the original image, so that the same region under different illumination conditions has similar spatial distribution. The experiments show that the algorithm not only has high matching accuracy, but also has good robustness to the illumination.

The rapid proliferation of electric vehicles (EVs) and the demand for faster charging times necessitate accurate energy metering across increasingly wider current ranges. Conventional single-meter approaches struggle to meet these demands, often exhibiting limited accuracy, particularly at high currents. To address this challenge, we present a novel energy metering model based on dual analog-to-digital converter (ADC) self-calibration. The model incorporates a zero-flux high-current monitoring technique and dual-ADC enhancement strategy, continuous current measurement. Additionally, a power gap compensation strategy, accounting for both switching-transition energy and conduction energy, enables accurate wide-range power metering. Experimental validation demonstrates a metering error of 1.29×10-2% across a 0-600 A range, achieving high-level accuracy of 0.02 with a measurement uncertainty of 0.017%. These results highlight the potential of our approach to provide robust, high-fidelity energy metering for next-generation EV charging systems.

The calibration of ultra-high voltage direct current (UHVDC) transmission systems, as an advanced electric power system that transmits electric energy with high efficiency and over long distances, is crucial to ensure the safe and stable operation of the system. However, due to the complexity and high technical requirements, it faces difficulties in calibration accuracy, calibration methods, and equipment. For this reason, this article proposes a digital twin model containing a physical system, data interaction, and a digital system, which adopts a two-layer feature extraction structure to recognize key devices through parameter perception. Then, the feature information of key devices is parametrically identified and the relative topological features are learned. Experimental results show that mean squared error, root mean squared error, and mean absolute error indexes are reduced by 71.08%, 46.32%, and 64.39%, respectively, and the R2 score is improved by 3.8% compared with the original method. It provides an important guarantee for the safe operation and reliability of the UHVDC system.