Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available

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

Harmonic current sideband indicators (HCSBIs) for broken bar detection and diagnostics in cage induction motors

Induction motor bar breakages have been increasingly studied in the last decades because of economic interests in developing techniques that permit on-line, non-invasive, early detection of motor faults in power plants. This work is specifically focused on broken bar detection and fault severity assessment in three phase power cage motors fed by non-sinusoidal voltage sources. In this work some new fault indicators for rotor bar breakages detection in squirrel cage induction motors are proposed, mathematically developed and experimentally proved. They are based on the sidebands of phase current upper harmonics, and they are well suited especially for converter-fed induction motors. The ratios I(7-2s)f/I5f and I(5+2s)f/I7f , I(13-2s)f/I11f and I(11+2s)f/I13f are examples of such new indicators, and they are not dependent on load torque and drive inertia, as classical indicators do. Their frequency-dependence has been also examined both theoretically and experimentally, and it was found less remarkable with respect to other indicators. Moreover, their values increase linearly with the quantity of consecutive broken bars, almost for not too much advanced faults; on 4-poles motors they were found quietly like the per-unit number of broken bars (ratio on total bar number). An original formulation is presented for motor mathematical modeling, based on the Generalized Symmetrical Components Theory, for sidebands amplitude computation. A complete motor model (involving all the elementary machine electrical circuits, as stator belts and rotor mesh loops) has been used for computer simulations; the same model was then transformed by using some complex Fortescue’s matrices to obtain a steady-state linear solution, solvable for stator and rotor currents, in healthy and faulty conditions. By exploiting the model, the formal definition of a set of new broken bar indicators was finally obtained. Machine simulations carried out by running the complete numerical model confirmed the accuracy of the model, and the theoretical previsions. Experimental work was performed by using a square-wave inverter-fed motor with an appositely prepared cage, for easy testing with increasing number of broken bars and without motor dismounting. Moreover, extensive experimentation was carried out on three industrial motors with different power and poles number, with increasing load, frequency and fault gravity for methodology validation. Finally, the ideas exposed in this work led to a patent application, owned by the University of Rome “Sapienza”

Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three Phase Stator Current Envelopes

A new method for the fault diagnosis of a broken rotor bar and interturn short circuits in induction machines (IMs) is presented. The method is based on the analysis of the three-phase stator current envelopes of IMs using reconstructed phase space transforms. The signatures of each type of fault are created from the three-phase current envelope of each fault. The resulting fault signatures for the new so-called ldquounseen signalsrdquo are classified using Gaussian mixture models and a Bayesian maximum likelihood classifier. The presented method yields a high degree of accuracy in fault identification as evidenced by the given experimental results, which validate this method

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

On the identifiability, parameter identification and fault diagnosis of induction machines

PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large variety of industrial applications. Although these machines are rugged and reliable, they are subjected to various stresses that might result in some unavoidable parameter changes and modes of failures. A common practice in induction machine parameter identification and fault diagnosis techniques is to employ a machine model and use the external measurements of voltage, current, speed, and/or torque in model solution. With this approach, it might be possible to get an infinite number of mathematical solutions representing the machine parameters, depending on the employed machine model. It is therefore crucial to investigate such possibility of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being used for parameter identification and fault diagnosis purposes. This project focuses on the identifiability of induction machine models and their use in parameter identification and fault diagnosis. Two commonly used steady-states induction machine models namely T-model and inverse Γ- model have been considered in this thesis. The classical transfer function and bond graph identifiability analysis approaches, which have been previously employed for the T-model, are applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm, the Alternating Conditional Expectation, is employed here for the first time to study the identifiability of both the T- and inverse Γ-models of the induction machine. The results obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel are non-identifiable while those of the inverse Γ-model are uniquely identifiable when using external measurements. The identifiability analysis results are experimentally verified by the particle swarm optimization and Levenberg-Marquardt model-based parameter identification approaches developed in this thesis. To overcome the non-identifiability problem of the T-model, a new technique for induction machine parameter estimation from external measurements based on a combination of the induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and inverter-fed operations show the success of the technique in identifying the parameters of the machine using only readily available measurements of steady-state machine current, voltage and speed, without the need for extra hardware. ii A diagnosis scheme to detect stator winding faults in induction machines is also proposed in this thesis. The scheme uses time domain features derived from 3-phase stator currents in conjunction with particle swarm optimization algorithm to check characteristic parameters of the machine and detect the fault accordingly. The validity and effectiveness of the proposed technique has been evaluated for different common faults including interturn short-circuit, stator winding asymmetry (increased resistance in one or more stator phases) and combined faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the accuracy of the proposed technique and it is ability to detect the presence of the fault and provide information about its type and location. Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out to verify the identifiability analysis and show the effectiveness of the proposed parameter identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase test induction machine coupled to a dynamometer loading unit and driven by a variable frequency inverter that allows operation at different speeds. All the experiment analyses provided in the thesis are based on terminal voltages, stator currents and rotor speed that are usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata- Misurata Universit

Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.Вступ. В даний час аналіз сигналу струму статора асинхронного двигуна став популярним методом оцінки стану працездатності асинхронного двигуна, щоб уникнути відмов. Класичні реалізації алгоритмів виявлення несправностей машин, що обертаються, засновані на мікропрограмних послідовних системах, таких як мікропроцесори і цифрова обробка сигналів, показали свої обмеження з точки зору швидкості та обмежень у реальному часі, що вимагає використання нових технологій, що забезпечують більш ефективну діагностику. наприклад, інтегральна схема для конкретної програми або програмована вентильна матриця (FPGA). Метою даної є дослідження внеску реалізації нечіткої логіки на програмованих логічних схемах FPGA в діагностику відмов асинхронних машин при несиметрії фаз і обривах фази. Методологія. У цій роботі ми пропонуємо апаратну архітектуру на FPGA алгоритму виявлення відмов асинхронної машини на основі нечіткої логіки та аналізу сигналів струму двигуна, приймаючи середньоквадратичний сигнал статора струму як сигнал індикатора несправності. Результати. Валідація запропонованої архітектури проводилася шляхом апаратного моделювання між платами ML402, оснащеними схемою Virtex-4 FPGA типу Xilinx та генератором системи Xilinx під керуванням MATLAB/Simulink. Оригінальність. Дана робота поєднала в собі ефективність методів нечіткої логіки, простоту алгоритмів аналізу сигналів струму статора та виконавчу потужність плати ML402 FPGA для діагностики несправностей асинхронних машин, досягаючи найкращих співвідношень швидкість/продуктивність та простота/продуктивність. Практична цінність. Поява цього методу покращила продуктивність виявлення несправностей асинхронної машини, особливо з точки зору споживання апаратних ресурсів, онлайн-виявлення в реальному часі та швидкості виявлення