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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 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.

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 aim of this paper is to estimate the iron losses for an induction machine in the healthy case taking the slotting effect into account and to study the effect of an inter‐turn short‐circuit on these losses. Theoretical results are then compared with experimental ones.

A simple analytical model of iron losses allows one to calculate and to appreciate the contribution of the slotting effect on induction machine iron losses without and with an inter‐turn stator short‐circuit. This semi‐analytical approach is based on the iron stator and rotor flux density repartition which is deduced from the air‐gap flux density.

The iron losses are not only due to the fundamental air‐gap flux density, but also to the slotting harmonics. In fact, the slotting effect generates harmonic flux density waves with very low magnitudes but with high‐angular velocities, leading to non‐negligible harmonic iron dynamic losses which have similar values on both the stator and the rotor. The inter‐turn short‐circuit generates an iron losses and a slotting harmonic contribution increase.

Experimental measurements give the total iron losses. They do not allow separating the fundamental and the slotting harmonics contribution.

The knowledge of the iron losses behaviour in the healthy machine taking into account the slotting effect is important to optimize the design. The fault contribution on these losses allows one to estimate the damage which can be engendered by the fault.

Generally, iron losses studies and calculations are performed numerically using finite element software. The analytical approach can be interesting because it allows one to make faster calculations and to analyze the influence of the machine geometric parameters.

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 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 introduce an algorithm based only on local extreme analysis of a time sequence to further the detection and diagnosis of inter-turn short circuits and unbalanced voltage supply using vibration signals.

The upper and lower extreme envelopes from a modulated and oscillatory time sequence present a particular characteristic being of, theoretically, symmetrical versions with regard to amplitude reflection around the time axis. Thus, one may say that they carry the same characteristics in terms of waveforms and, consequently, frequency content. These envelopes can easily be built by an interpolation process of the local extremes, maximums and minimums, from the original time sequence. Similar to modulator signals, they contain more detailed and useful information about the required electrical fault frequencies.

Results show the efficiency of the proposed algorithm and its relevance to detecting and diagnosing faults in induction motors with the advantage of being a technique that is easy to implement in any computational code.

A laboratory investigation carried out through an experimental setup for the study of faults, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance, is presented.

The main contribution of the work is the presentation of an alternative tool to demodulate signals which may be used in real applications like the detection of faults in three-phase induction machines.

The objective is the application of vibration analysis for the detection and diagnosis of low isolation between the stator coil wind and the voltage phase unbalance in induction motors with different numbers of poles. The purpose of this paper is to provide an approach for maintenance engineers for diagnosis electrical fault through the vibration analyses.

A detailed review of previous work carried out by some researchers and maintenance engineers in the area of machine fault detection is performed. By vibration analysis, the spectra were collected, which used to analyze the failure. Vibration spectra could detect particular characteristic for each fault in an initial condition, so the machine health can be preserved.

Results show the efficiency of the technique of vibration analysis and their relevance to detect and diagnose faults in different induction motors. In this way, it may be included in future predictive maintenance programs.

The paper presents a laboratory investigation carried out through an experimental set-up for the study of fault, mainly related to the stator winding inter-turn short circuit and voltage phase unbalance.

The main contribution of the paper has been the characterization of one more tool that makes the predictive maintenance process more efficient, effective and faster, increasing the reliability and availability of equipment.

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.