A Model for Induction Motors with Non-Uniform Air-Gap

Equations to calculate inductances of induction motors, considering non-uniform air-gap, are proposed. The analyzed air-gap variations are static and dynamic eccentricity and stator slots. The equations for inductance calculation, obtained from the modified winding functions and the energy stored in the air-gap, allow considering the effect of rotor bar skewing. Experimental results that validate the proposed method are presented.Fil: Bossio, Guillermo Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Grupo de Electrónica Aplicada; ArgentinaFil: de Angelo, Cristian Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Grupo de Electrónica Aplicada; ArgentinaFil: Solsona, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Grupo de Electrónica Aplicada; ArgentinaFil: Valla, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentin

A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals

Motor current signature analysis (MCSA) has been an effective way of monitoring electrical machines for many years. However, inadequate accuracy in diagnosing incipient broken rotor bars (BRB) has motivated many studies into improving this method. In this paper a modulation signal bispectrum (MSB) analysis is applied to motor currents from different broken bar cases and a new MSB based sideband estimator (MSB-SE) and sideband amplitude estimator are introduced for obtaining the amplitude at (1±2s)fs(1±2s)fs (s is the rotor slip and fsfs is the fundamental supply frequency) with high accuracy. As the MSB-SE has a good performance of noise suppression, the new estimator produces more accurate results in predicting the number of BRB, compared with conventional power spectrum analysis. Moreover, the paper has also developed an improved model for motor current signals under rotor fault conditions and an effective method to decouple the BRB current which interferes with that of speed oscillations associated with BRB. These provide theoretical supports for the new estimators and clarify the issues in using conventional bispectrum analysis

A Review of Techniques Used for Induction Machine Fault Modelling

[EN] Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given faultThis work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i-Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE)Terrón-Santiago, C.; Martinez-Roman, J.; Puche-Panadero, R.; Sapena-Bano, A. (2021). A Review of Techniques Used for Induction Machine Fault Modelling. Sensors. 21(14):4855-4873. https://doi.org/10.3390/s21144855S48554873211

Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe

Modeling of Balanced and Unbalanced Three-Phase Induction Motor under Balanced and Unbalanced Supply Based on Winding Function Method

An accurate model of balanced and unbalanced three-phase Induction Motor (IM) under balanced and unbalanced supply conditions based on Winding Function Method (WFM) is presented in this work. In this paper, the unbalanced condition in three-phase IM is limited to stator winding open-phase fault. The analysis of presented models is shown in details which allow predicting the performance of 3-phase IM under different conditions. Computer simulations were obtained using the MATLAB software for a three-phase squirrel cage IM. MATLAB simulation results show that the oscillation of the speed and electromagnetic torque has increased considerably due to the open-phase fault in stator windings

Detection and Diagnosis of Compound Faults in Induction Motors Using Electric Signals from Variable Speed Drives

As a primer driver, induction motors are the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal statues and avoid excessive energy consumption and severe damage to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar faults under closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives more correct indication of the fault severity and location for all operating conditions

Diagnosis of induction machines by parameter estimation

International audienceThe type of control system used for electrical machines depends on the use (nature of the load, operating states, etc.) to which the machine will be put. The precise type of use determines the control laws which apply. Mechanics are also very important because they affect performance. Another factor of essential importance in industrial applications is operating safety. Finally, the problem of how to control a number of different machines, whose interactions and outputs must be coordinated, is addressed and solutions are presented. These and other issues are addressed here by a range of expert contributors, each of whom are specialists in their particular field. This book is primarily aimed at those involved in complex systems design, but engineers in a range of related fields such as electrical engineering, instrumentation and control, and industrial engineering, will also find this a useful source of information

Modulation signal bispectrum analysis of electric signals for the detection and diagnosis of compound faults in induction motors with sensorless drives

As a prime driver, induction motor is the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal status and avoid excessive energy consumption and severe damages to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar (BRB) faults under closed loop operation modes. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives a more correct indication of the fault severity and its location for all operating conditions