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This paper presents surrogate-model based optimization for synchronous reluctance motor (SynRm) with transversally laminated rotor. A radial basis function (RBF) model with 12 input variables and three outputs is first trained. A dataset is obtained using finite element method to estimate parameters of RBF model. By building RBF model, the RBF network can predicts the outputs of the SynRm with good accuracy Using non-dominated sorting genetic algorithm (NSGA II), pareto front is obtained. The SynRm is designed to maximize the maximum developed torque and power factor of the motor with constrained torque ripple.

motors. Electrical motors are crucial components in various industrial applications, and their efficient operation is essential for maintaining productivity and minimizing downtime. Traditional fault diagnosis methods have limitations in accurately detecting and classifying motor faults. Deep learning, a subset of machine learning, has emerged as a promising approach for improving fault diagnosis accuracy. This review discusses various deep learning methods, such as convolutional neural networks, recurrent neural networks, autoencoders, transfer learning, and transformers that have been utilized for motor fault diagnosis. Additionally, it examines different datasets and features used in these methods, highlighting their advantages and limitations. The paper also discusses challenges and future research directions in this field, such as data augmentation, transfer learning, and interpretability of deep learning models. Based on the findings, it is concluded that deep learning-based technologies are replacing manual expert involvement as the new norms in this field. Besides, methods are getting more standard, and official benchmarks are being created. A summarized table is provided at the end of the paper and numerous methods have been reported. [ABSTRACT FROM AUTHOR]

The spoke‐type arrangement of permanent magnets (PMs) in rotor of Vernier PM (VPM) machines was first introduced in a dual‐stator topology to overcome low‐power drawback of VPM machines. However, this structure is mostly applicable for high‐power motor drives. A non‐ferromagnetic spoke array VPM generator with a special rotor structure for small‐scale wind turbine generator is presented. The leakage flux lines tend to be aligned with the main flux routes due to a special design of flux barriers in end portion of PM housings. High torque‐density, low weight, torque‐speed curve with a wide constant torque region, promising efficiency, and power factor are the advantages of the proposed machine. An Equivalent‐Magnetic‐Network (EMN) model is established for predicting the complex behaviour of flux lines and operating quantities of the proposed structure with reasonable accuracy. The contribution of the modelling method includes a special mesh cell for the air‐gap permeance network to more accurately capture the flux routes, continuous positioning method for omitting spikes on post‐processing waveforms due to numerical errors, and a stable solving flowchart with a high convergence rate. The validity of the EMN model is validated by comparing the operating waveforms of the machine with the corresponding waveforms obtained by the finite element and experiments. The cover image is based on the Research Article Design of a non‐ferromagnetic spoke array Vernier permanent magnet generator with iron‐bar based on enhanced equivalent magnetic network model by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12272. [ABSTRACT FROM AUTHOR]