This paper is focused on the summary of induction motor torque ripple theories which is braked by dynamometer. The main aim of this paper explains the causes of the creations of harmonics components contained in the torque of the induction motor during steady state or dynamic motor operation. Furter point of this paper is separation of torque components between measured motor and dynamometer because the torque is measured by one sensor situated on the same coupled shaft common for both machines. Practical, it is the torque analysis with respect of motor and dynamometer topology and both machines are supplied by sinusoidal source only.

The article describes the methodology for calculating the heating of an induction machine with an asymmetrical power supply. Parasitic torques and the effect on the machine torque curve are shown. The goal is to determine how long the motor can still be operated with a given voltage asymmetry before the allowable temperature is exceeded.

In this article, a forward uncertainty propagation method is presented for a 2-D finite-element (FE) model in an induction machine. This method is applied to quantify the uncertainty of input parameters, for example, dimensions and material properties, and demonstrate their variability effect on harmonics related to the broken rotor bar (BRB) faults. To show the most influential input parameters in the case of BRB harmonics, a global sensitivity analysis is performed from the polynomial chaos expansion (PCE) approximation of the FE model. The results of this study indicate that BRB harmonics are highly sensitive to stator inner diameter, rotor outer diameter, rotor bar conductivity, and core materials. Moreover, the combined variability of these sensitive input parameters can attenuate the amplitude of the BRB harmonics 30%–90% compared to the simulation results at nominal values of input parameters and closely match with measurement results.

This article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features' importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (LOLO-CV). Leveraging LOLO-CV, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%.

In the paper, electromagnetic and vibration analyses of traction electric machine exhibiting static and dynamic eccentricities is performed. The machine is a highspeed permanent magnet synchronous motor with surface-mounted permanent magnets and double-layer concentrated winding. The machine is intended for use in a compact drive unit in the applications such as light rail vehicles, trams or metro wagons. Vibration of electromagnetic origin are examined using finite element analysis and referenced to the machine in a perfect technical condition. Moreover, impact of the both eccentricities on stator phase currents and torques is analyzed.

This paper presents a fault diagnosis scheme for induction machines (IMs) using Support Vector Machine (SVM) and Random Forests (RFs). First, a number of time domain and frequency-domain features are extracted from vibration and current signals in different operating conditions of IM. Then, these features are combined and considered as the input of SVM-based classification model. To avoid overfitting, RF is utilized to determine the most dominant features contributing to accurate classification. It is proved that the proposed method is capable of achieving highly accurate fault diagnosis results for broken rotor bar and eccentricity faults and it can appropriately handle the high dimensionality of the combined data.

The paper deals with the squirrel cage of an induction motor. Faulty connection between the rotor bar and the end-ring has a fundamental effect on the magnitude of the current of an affecting bar and surrounding bars as well. By the matrix equation is solved the current distribution in the damaged bars of a squirrel cage. The thermal dilatation of the bars is also observed. Unsymmetrical thermal stress on the bars causes them to different dilatation and leads to further development of the fault.

The paper deals with magnetic forces and vibration analysis of a nine-phase induction motor. It is described a method of voltage harmonics injection for reaching either the same amplitude or RMS value of supplying voltage as for the case of supplying the motor using the voltage fundamental only. Influence of the both types of voltage harmonics injection on the magnetic forces and motor vibration is evaluated using finite element analysis and compared to the motor supply by sinusoidal voltage.

In the paper, authors deal with the case study of an impact of stator frame and vertical mounting on modal and vibration behavior of a squirrel-cage induction machine. The study is performed using finite element analysis where resulting magnetic forces from magnetic transient analysis are set as an excitation in the analysis of harmonic response. Modal analysis is carried out to identify the modes and natural frequencies of the modeled structures and their possible effect on vibration spectra. Resulting vibration spectra are compared to the analysis of a solely stator lamination and to the experimental results on the full machine assembly.

The paper compares characteristics of three steel types - one standardized in EN 10106 (M235-35A) and two special steel alloys - Arnon5 and Hiperco 50. The iron core loss characteristics are derived to describe the dependence of loss number on magnetic flux density and frequency. Based on obtained results the application field of each material is discussed.

The paper deals with the experimental verification of vibration frequency spectra of squirrel-cage induction machine having implemented static eccentricity fault. The measured data are compared to the theoretical presumptions and the transient finite element analysis of the measured machine. Measurement and finite element model are carried out for the machine in a perfect technical condition as well and those specify the reference point for the faulty machine.

This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

The autotransformer which is supplied by network without neutral wire must be loaded symmetrically. Already small unsymmetrical load causes significant voltage imbalance and overvoltage. When the fully loading of one phase is required, the zigzag autotransformer connection must be used. This paper describes the behavior and design of autotransformers under unsymmetrical loading conditions.

The paper is focused on the experimental static eccentricity diagnostics using acoustic noise frequency spectra. The presented study presumes induction machine operating under constant load in an environment with steady acoustic noise producing facilities. The frequency spectra analyses are performed in a frequency range up to 1500 Hz allowing validation of the acoustic noise analyses by the vibration measurement.

The paper is focused on the comparison of vibration and emitted noise measurement of induction machine under static eccentricity in an environment with steady acoustic noise producing facilities. Such an environment can introduce for example simple industrial drive stand operating under constant load in a closed room. The analyses are focused to the frequency spectrum up to 1500 Hz which allows us to validate the frequency spectrum of emitted noise by vibration measurement. The analyzed frequency band is sufficient considering vibration response of implemented fault. The analyses of healthy machine are carried on as well to get the reference point.

The subject of this paper is the calculation of the induction machine air gap flux density distribution. Following previous paper, the parameters affecting the distribution are integrated to create the final no-load flux density distribution. The no-load calculations are valid for every type of winding and can be applied to almost every type of electric machine. To calculate the flux density distribution in loaded condition, the magnitude and phase shift of the rotor current have to be calculated. The rotor magnetomotive force reaction distribution is calculated according to the stator calculations. Based on the equivalent circuit of the machine, the flux density distribution of variable load condition can be calculated. All numerical results are compared with the finite element analysis calculations and the distribution and spectral analysis of the air gap flux density are evaluated in this paper.

The subject of this paper is evaluating and comparing of the measured mechanical losses with equation for mechanical losses calculation. The electric machines have numerous sources of losses which are converted into the heat. These losses heat up the machine and have an impact on the total efficiency. There is an effort to reduce the losses to a minimum value without increasing the size and the weight of the machines. In this paper, two induction machines are compared by using analytical formulas for determining the mechanical losses. Thereafter the measured values are compared with existing formulas and their accuracy is evaluated. Furthermore the overall deviance of the result in the calculation and design of the machine is discussed. The percentage of the total losses and the influence on the efficiency of the machine is used to demonstrate the effect of the error of the measurement.

In the case of rotating electrical machines, the magnetic forces acting on the stator teeth are the principal electromagnetic cause of vibrations. Based on this fact, this paper presents a method to compute the vibrations of an induction motor with the aid of magnetic nodal forces. An accurate computation of local or nodal forces is essential in problems pertaining to vibration and noise analysis of electrical machines. Virtual work method is utilized here to compute the nodal forces as the local derivative of magnetic energy from the Finite Element (FE) solution of the magnetic field problem. The magnetic problem is then coupled to an elasticity solver to calculate the displacement due to these forces. The nodal force method is implemented in an open source finite element software Elmer and the entire magneto-mechanical computation is carried out in the same open source tool. The calculated results are then compared to vibration measurements of the motor.

This paper analyzes the mechanical vibration frequency spectra of a healthy squirrel cage induction machine and the identical machine operating under the dynamic rotor eccentricity. Radial vibrations are evaluated based on the experimental data in no-load and nominal load steady state operations. The main attention is paid to the rotational frequency and twice line frequency vibration components. Necessity of further study is pointed out.

The paper analyzes a possible source of destructive rotor vibration of high-power induction motor working as drive for water pump in power plant. The machine has no available documentation and therefore the winding design and the power balance are both made prior the investigation of vibration source. Based on this analyses the recommendations for the rotor overhauling are given.