This article proposes an interturn short-circuit fault diagnosis method for permanent magnet synchronous machines for electric vehicles based on search coils (SC) with a unique structure. In this proposed structure, the SCs are uniformly wound in the main flux direction of each phase, and the number of SCs is twice the number of phases, resulting in significant cost savings. Based on this structure, a novel fault feature based on the negative-sequence components of the second carrier frequency sideband harmonics in the voltage signal of the SC is proposed. As a result, this fault characteristic frequency component is not only more obvious than the fundamental component used in the traditional SCs method but also can be transformed to a dc indicator by two coordinate transformations, which is conducive to online implementation in both stationary and nonstationary conditions. Finally, experiments are presented to verify the proposed diagnosis strategy.

In this article, a novel rotor fault diagnosis and separation approach based on search coils (SCs) for permanent magnet synchronous motors is proposed. First, the SC setup is introduced along with its working principle and spatial arrangement. Based on this special structure, the local magnetic field distortion caused by the local demagnetization fault (LDF) of permanent magnets or the dynamic eccentricity fault (DEF) can be accurately diagnosed. The fault characteristics of both LDF and DEF are the existence of fractional harmonics in the SC induced voltage signal. Compared to the traditional phase back electromotive force method, these features do not depend on the motor pole-slot combination and are not be hidden due to periodic overlap. Second, to distinguish between two faults with similar characteristics and considering the inherent errors of healthy motors due to processing tolerances, two random forest (RF) models are chosen to gain automated classification. Finally, to simplify the model and improve diagnostic efficiency, the features are screened and the final diagnosis scheme is established. Simulation and experimental results have validated the accuracy and the superiority of the proposed method.

This article proposes a diagnosis method for six types of rotor and bearing faults of permanent magnet synchronous motor based on acoustic signals, and builds a digital twin system for two-stage diagnosis on the basis of an Internet of Things component and the cloud. The first stage is located at the edge of the motor, only short-term Fourier transform and convolutional neural network are used to distinguish fault state from healthy state. By comparing the performance of different frequency bands, a scheme with the lowest hardware requirements is optimized and designed, reducing the number of network nodes by 100 times and the sampling frequency by 12 times. The second stage is located in the cloud, which fully utilizes the data-efficient image transformer to recognize the multidimensional images converted from acoustic signals, thereby achieving the identification of different faults. These images contain ten acoustic features, which are specifically layered and arranged in the Red, Green, and Blue channels of the image to improve the accuracy of fault identification and anti-interference ability. Finally, experimental verification shows that the diagnostic scheme exhibits excellent performance in terms of accuracy and computational resource utilization.

The acoustic signal contains a wealth of features and information, which can be used as a promising substitute for vibration signals in some cost-limited applications for motor fault diagnosis. This work presented a method involving the detection of motor rotor faults using acoustic signals, to diagnose the demagnetization faults and the eccentricity faults. The acoustic signals in different motor conditions are first de-noised by wavelet packet transform to remove the irrelevant components, then the MFCCs are extracted as the features by the reason of satisfactory performance in sound recognition. Next, the features are converted into 2D images, and the fault diagnosis and classification are realized by the DeiT classifier. Experiments show that compared with other time-frequency domain analysis, e.g., HHT, or the processing method that does not convert to 2D feature images, the accuracy of the proposed method is the highest, reaching 99.26%, proving that the method is reliable and effective for accurate fault diagnosis in a non-invasive manner.

A torque closed-loop flux-weakening (FW) control strategy of interior permanent magnet synchronous motor (IPMSM) based on search coils (SCs) is proposed in this article. The proposed strategy adopts a three-closed-loop control structure, where the torque closed-loop is constructed to improve the torque control accuracy, then the voltage vector angle is controlled by the torque regulator to maximize the dc-link voltage utilization, and finally, the current control model is constructed from a novel view and enables the dq -axis current to be controlled by the qd -axis voltage regulator. Subsequently, an improved torque estimation method is proposed, which utilizes the motor magnetic field information collected by SCs for torque estimation. The estimation accuracy of the method is not affected by the motor parameters and operating state because the flux linkage of the motor can be directly observed. Ultimately, the combination of torque, voltage, and current loops enables the motor to operate adaptively at the optimal operating point without the need for motor parameters, as well as maintains accurate torque output and improves the efficiency and torque output capability of the motor. Finally, the feasibility of the proposed method is verified by experiments.

Estimating rotor temperature is essential for precise torque control and over-temperature warning in induction motors. Therefore, a rotor temperature estimation strategy is proposed in this study. Firstly, a three-order lumped-parameter temperature network is developed to observe the rotor temperature online, and parameters of thermal network are identified offline by genetic algorithm. However, the actual air-gap thermal conductance and end-space thermal conductance varies with operating conditions, and it would be unfavorable for temperature estimation if the thermal conductance is kept constant during operation. To address this issue, an adaptive law for air-gap thermal conductance and a temperature error feedback compensation matrix were designed using the Lyapunov stability theory. The advantage of the rotor temperature estimation strategy is that the error feedback of the stator temperature can be used to realize the correction of the air-gap thermal conductance. The temperature experiments show that the rotor temperature estimation strategy has higher temperature estimation accuracy than conventional lumped-parameter temperature network. Meanwhile, torque experiments demonstrate that the torque control accuracy of the indirect magnetic field directional control is significantly improved.