This paper proposes turn switching along with winding switching for high efficiency of permanent magnet vernier machines (PMVM) during wide speed range operation for electric vehicles application. For winding switching, the three-phase winding of the machine is divided into two sets of windings, namely, winding set A, B, C and winding set X, Y, Z. For turn switching, the number of turns for one set of winding is changed. With different number of turns for winding set X, Y, Z, the efficiency of the machine can be significantly improved. Initially, the basic electromagnetic characteristics, such as the flux linkage, back electromotive force (EMF), torque, and phase voltages of the machine are investigated using winding switching and turn switching. Then, the output power, torque speed curve, core losses, efficiency, and power factor of the machine are analyzed in detail using different number of turns. In addition, the transient effects of winding switching and turn switching has been analyzed. Finally, a control strategy is proposed which combines turn switching and winding switching to ensure high efficiency of PMVMs during wide speed range operation. A PMVM is designed and driven in accordance with proposed control strategy (PCS) to show the characteristics of PCS. The PMVM driven with PCS is compared to a recently presented PMVM. The recently presented PMVM is driven by a different control strategy and is analyzed with two different kind of steel laminations that is general steel (50TW470) and special steel (20JHF1300).

In the last decade, permanent magnet (PM)-free or hybrid PM machines have been extensively researched to find an alternative for high cost rare-earth PM machines. Brushless wound rotor synchronous machines (BL-WRSMs) are one of the alternatives to these PM machines. BL-WRSMs have a lower torque density compared to PM machines. In this paper, a new topology is introduced to improve the torque producing capability of the existing BL-WRSM by utilizing the vacant spaces in the rotor slots. The new topology has two harmonic windings placed on the rotor which induce separate currents. A capacitor is used between the two harmonic windings to bring the currents in phase with each other. The harmonic winding currents are fed to the rectifier which is also placed on the rotor. Due to additional harmonic winding, the overall field current fed to the rotor field winding has been increased and hence the average torque has also increased. Finite element analysis (FEA)-based simulations are performed using ANSYS Maxwell to validate the proposed topology. The results show that the average torque of the machine has been significantly increased compared to the reference model. The detailed comparison results are provided in this paper.

Predictive maintenance is increasingly important in rotating machinery to prevent unexpected failures, reduce downtime, and improve operational efficiency. This study compares the efficacy of traditional machine learning (ML) and deep learning (DL) techniques in diagnosing bearing faults under varying load and speed conditions. Two classification tasks were conducted: a simpler three-class task that distinguishes healthy bearings, inner race faults, and outer race faults, and a more complex nine-class task that includes faults of varying severity in the inner and outer races. In this study, the machine learning algorithm ensemble bagged trees, achieved maximum accuracies of 93.04% for the three-class and 87.13% for the nine-class classifications, followed by neural network, SVM, KNN, decision tree, and other algorithms. For deep learning, the CNN model, trained on scalograms (time–frequency images generated by continuous wavelet transform), demonstrated superior performance, reaching up to 100% accuracy in both classification tasks after six training epochs for the nine-class classifications. While CNNs take longer training time, their superior accuracy and capability to automatically extract complex features make the investment worthwhile. Consequently, the results demonstrate that the CNN model trained on CWT-based scalogram images achieved remarkably high classification accuracy, confirming that deep learning methods can outperform traditional ML algorithms in handling complex, non-linear, and dynamic diagnostic scenarios.

This letter proposes winding switching for the high‐performance of permanent‐magnet vernier machines (PMVMs) in wide speed range operation. The machine is operated in two modes: cumulative and differential. Before winding switching (cumulative mode), the machine is operated like a conventional three‐phase PMVM. The machine provides high torque density and high efficiency in this mode. After winding switching (differential mode), the back EMF and inductance of the machine are decreased significantly, which enable wide speed range operation and the attainment of high power factor in high‐speed operation.

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics.

This paper offers a novel dual-mode double stator wound rotor synchronous machine for variable speed applications. The proposed motor integrates the benefits of both the traditional wound rotor synchronous machine (WRSM) and brushless wound rotor synchronous machine (BL-WRSM). A constant torque can be attained in the maximum torque per ampere region by operating the proposed machine as a traditional WRSM in Mode I, and a constant power can be attained in the field-weakening region by operating it as a BL-WRSM in Mode II. Moreover, due to the dual-stator structure, the proposed machine exhibits improved performance in terms of high torque density as compared to the existing single stator BL-WRSM. By using a special stator winding arrangement to achieve the sub-harmonic component of the stator magnetomotive force, the brushless operation of the proposed machine is achieved. The additional sub-harmonic component induces a voltage in the harmonic winding placed on the rotor, which is then rectified and provided a DC current to field winding for brushless excitation. In order to validate the effectiveness of the proposed machine, a two-dimensional finite element analysis (FEA) is carried out.