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The impact of the loop current (LC) on the motor magnetic field in the analysis of the inter-turn short circuit (ITSC) fault is always ignored. This paper made a comparative study on the electromagnetic field of permanent magnet synchronous motors (PMSM). The purpose of this study is to explore the necessary of the LC existing in the fault analysis and the electromagnetic characteristics of the PMSM with the ITSC fault when taking into account the LC.

Based on the finite element method (FEM), the fault model was established, and the magnetic density of the fault condition was analyzed. The induced electromotive force (EMF) and the LC of the short circuit ring were studied. The three-phase induced EMF and the unbalance of the three-phase current under the fault condition were studied. Finally, a prototype test platform was built to obtain the data of the fault.

The influence of the fault on the magnetic density was obtained. The current phase lag when the ITSC fault occurs causes the magnetic enhancement of the armature reaction. The mechanism that LC hinders the flux change was revealed. The influence of the fault on the three-phase-induced EMF symmetry, the three-phase current balance and the loss was obtained.

The value of the LC in the short circuit ring and the influence of it on the motor electromagnetic field were obtained. On the basis of the electromagnetic field calculation model, the sensitivity of the LC to the magnetic density, induced EMF, current and loss were analyzed.

The purpose of this paper is to obtain an integrated method for inter-turn short circuit fault detection for the cage-rotor induction machine (CRIM) considering saturation effect.

The magnetic equivalent circuit (MEC) is proposed for machine modeling and nonlinear B-H curve is considered for saturation effect. The machine has some differential equations which are converted to algebraic type by trapezoidal method. On the other hand, some nonlinear equations are present due to saturation effect. A set of nonlinear algebraic equation should be solved by numerical method. Therefore, the Newton-Raphson technique is used for equation solving during of the considered time step.

Generally, the operating point of electrical machines is close to the saturation zone due to designing considerations. Moreover, some current and torque harmonics will be produced due to time and space harmonics combination, which cannot be studied when saturation modeling is neglected. Considering both space and time harmonics, a method is proposed for inter-turn short circuit fault detection based on the stator current signatures and the machine performance is analyzed in healthy and faulty cases. In order to obtain the integrated method, two sample machines (two and also four-pole machines) are modeled and finally the accuracy of the proposed method is verified through the experimental results.

The calculations have been done in this work is limited to CRIM considering. However, the presented modeling method can be used for another types of electrical machines by some minor modifications.

Obtaining of an integrated formula for the inter-turn short circuit fault detection which has been presented for first time is the more advantages of present work. Moreover, in order to saturation effect considering, a new method is presented for solving of nonlinear equations which is another novelty of paper.

Diagnostics of electrical machines is a very important task. The purpose of this paper is the presentation of coupling three numerical techniques, a finite element analysis, a signal analysis and an artificial neural network, in diagnostics of electrical machines. The study focused on detection of a time-varying inter-turn short-circuit in a stator winding of induction motor.

A finite element method is widely used for the calculation of phase current waveforms of induction machines. In the presented results, a time-varying inter-turn short-circuit of stator winding has been taken into account in the elaborated field-circuit model of machine. One of the time-varying short-circuit symptoms is a time-varying resistance of shorted circuit and consequently the waveform of phase current. A general regression neural network (GRNN) has been elaborated to find a number of shorted turns on the basis of fast Fourier transform (FFT) of phase current. The input vector of GRNN has been built on the basis of the FFT of phase current waveform. The output vector has been built upon the values of resistance of shorted circuit for respective values of shorted turns. The performance of the GRNN was compared with that of the multilayer perceptron neural network.

The GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network.

It is argued that the proposed method based on FFT of phase current and GRNN is capable to detect a time-varying inter-turn short-circuit. The GRNN can be used in a health monitoring system as an inference module.

The purpose of this paper is to present a study and analysis of insulation failure inter‐turn fault in induction machines (IMs).

A time stepping finite element method (FEM) analysis is performed for the study of IM with inter‐turn fault and determining the machine parameters (self and mutual inductances) after occurring fault. A simple dynamic model for IM with inter‐turn fault is presented. The model parameters are obtained by FEM analysis. An experimental test is also carried out to verify the results.

The behavior of IM is studied under various insulation failure inter‐turn fault conditions and severity using FEM. The paper's results help the machine designers to improve the fault tolerance as well the overall design of the machine drive system. It can also be useful for predict and detection of fault in IM.

Predicting and detection of turn faults in IM are in industry very helpful because it avoids the fully damage of IM and it is more easy to repair the machine. Designing a fault tolerant IM is required in some applications for increasing the reliability.

By using FEM for studying the fault, the machine parameters which are calculated with FEM and the study's results are very precise and accurate because the flux fluctuation after occurring fault has been taken into account. On the other hand, the fault model is very fast, global and accurate. It can be used in model‐based health monitoring systems.

The purpose of this paper is to present a new model to study inter‐turn short circuit faults in a permanent magnet synchronous machine.

The machine is modeled by using classical two‐axis theory, and the equations are modified to take into account the stator inter‐turn faults. A state space form of the system is presented for dynamic simulations.

The machine model is global and can work in both normal and fault conditions due to a fictitious resistance in the winding circuit. Various simulation results have been presented indicating the fault instant and its corresponding effect. Validation is carried out by transient time finite element simulations.

The model can serve as a step towards development of fault detection and diagnosis algorithms.

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

The aim of this paper is to propose the model for analyzing the electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions.

The model uses interconnected reluctance network formed based on the geometrical approximations to predict magnetic performances of the machine. The network consists of several types of reluctances for modeling different parts of machine. Applying Kirchhoffs laws in the network and the machine windings, magnetic and electrical equations are obtained, respectively. To construct the model system of equations, the electrical equation is converted into algebraic form by using the trapezoidal technique. Moreover, the system of equations must be solved by Newton–Raphson method in each step-time because of considering the core saturation effect.

The proposed model is developed based on the modified magnetic equivalent circuit (MEC) method, in which the number of flux paths in different parts of the machine can be arbitrary selected. The saturation effect, skewed slots, the desired machine geometrical parameters and various winding arrangements are included in the proposed model; therefore, it can evaluate the time and space harmonics in modeling the PMVMs. Furthermore, a pattern for inter-turn fault detection is extracted from the stator current spectrum. Finally, 2 D-finite element method (FEM) and 3 D-FEM analysis are carried out to evaluate and verify the results of the proposed MEC model.

Generally, the element numbers have important role in modeling the machine and calculating its performance. Hence, the proposed MEC model’s capability to choose desired number of flux paths is advantage of this paper. Moreover, the developed MEC can be used for analyzing several electrical machines, including other types of vernier machines, with simple modification.

This paper aims to introduce the usage of sensitivity analysis (SA) for the problem of faults identification in three-phase induction motors (IMs). These motors are susceptible to different kinds of faults that should be detected in a proper time to keep the systems working in a safety environment.

One of the effective approaches for faults identifications, which is presented in the literature, is a model-based strategy. This strategy mainly depends on using a software model to make an identification decision. Therefore, this work intends to examine the model sensitivity towards variables’ variation. The SA toolbox of Matlab R2017b package is used for this purpose since the Matlab software is a well-known environment, and it is easy for a nonstatistical person to deal with it. As a study case, open-circuit and stator inter-turn faults in the stator windings of a three-phase IM have been chosen.

The results show that the model-based strategy is considerably speed up by up to 30% when neglecting the trivial model’s parameters with the same accurate identification decision as compared with the results of this strategy without using the SA.

The novelty of this work is summarized in devoting the usage of SA in the field of faults identification to enhance the speed of final decision.

This paper aims to go deeper on the analysis of the shaft voltage of large turbogenerators. The main interest of this study is the investigation process developed.

The analysis of the shaft voltage because of several defects is based on a two-dimensional (2D) finite element modeling. This 2D finite element model is used to determine the shaft voltage because of eccentricities or rotor short-circuit.

Dynamic eccentricities and rotor short circuit do not have an inherent impact on the shaft voltage. Circulating currents in the stator winding because of defects impact the shaft voltage.

The original value of this paper is the investigation process developed. This study proposes to quantify the impact of a smooth stator and then to explore the contribution of the real stator winding on the shaft voltage.

On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.

In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.

The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].

In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.