This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.

Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research has been focused on evaluating deep learning algorithms for the automatic diagnosis of these faults. Therefore, the purpose of this study is to propose a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of the stator current and the external stray flux measured on the induction motor in which the bearings are installed. All the data were collected by performing experimental tests in the laboratory. Then, these data were processed to obtain images (scalograms and spectrograms), which were elaborated by a pre-trained Deep Convolutional Neural Network, modified through the transfer learning technique. The results demonstrated the ability of the electromagnetic signals, and in particular of the stray flux, to detect bearing faults and mechanical anomalies, in agreement with the recent literature. Moreover, the Convolutional Neural Network has been proven to be able to automatically discriminate bearing defects and with respect to the healthy condition.

A new procedure for the design and optimization of the rotor laminations of a synchronous reluctance machine is presented in this paper. The configuration of the laminations is symmetrical and contains fluid-shaped barriers. The parametrization principle is used, which executes variations in the lamination geometry by changing the position, thickness and shape of the flux barriers. Hence, the optimization procedure analyzes the various configurations through finite element simulations, by means of the communication between MATLAB and Flux 2D. In the post processing stage, the best geometry which optimizes mean torque, torque ripple, efficiency and power factor is selected. Once the best rotor configuration is defined, further investigations allow improving its performance by modifying the current angle, the stator winding and the thickness of the radial ribs.

In traction, automotive, and some industrial processes, the operation of the induction motor is set using speed. This creates an additional control loop that is external to the current controller that inverters already possess. While the impact of the latter control loop on fault detection in induction motors has already been studied, the influence of closed-loop speed control has not been explored enough. This paper presents the findings on the impact of PID speed control on the broken rotor bar fault detection. The study reveals that the constant corrective actions of the PID controller to correct the speed cause an increase in the noise level and spectral leakage around the first harmonic in the stator current spectrum that prevents fault detection. It is concluded that it is necessary to use time-frequency analysis techniques with excellent spectral resolution, such as the Dragon Transform or the Min-Norm technique.

The global decarbonization targets require increasingly higher levels of efficiency from the designers of electrical machines. In this context, the opportunity to employ magnetic or semi-magnetic wedges in surface-mounted permanent magnet machines with fractional-slot concentrated winding has been evaluated in this paper, with the aim to reduce the power losses, especially in the magnets. Since an analytical calculation is not sufficient for this evaluation, finite element methods with two different software have been employed, by using a model experimentally validated on a real motor. The effects of wedges with different values of permeability and different magnetization characteristics have been evaluated on flux density, back electromotive force, and inductances, in order to choose the more suitable wedge for the considered motor. Furthermore, a new wedge consisting of different portions of materials with different magnetic permeability values is proposed. The effects of both conventional and unconventional magnetic wedges were assessed to optimize the motor performance in all working conditions.

In this paper, an experimental investigation about the use of non-invasive instruments, like stator current and external leakage flux analysis, to detect inter-turns short-circuits in stator windings of low voltage induction motors supplied by inverter is reported. The results are encouraging and the technique here proposed can be evaluated as a tool to detect these faults in their early stage in motors which are more stressed with respect to those supplied by the mains, due to the IGBT PWM feeding.