Comprehensive and Simplified Fault Diagnosis for Three-Phase Induction Motor Using Parity Equation Approach in Stator Current Reference Frame (original) (raw)
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Fault Diagnostic Based on Parity Equations Applied to Induction Motor
Resumen: En este trabajo se presenta una técnica de detección de fallas basada en el análisis con ecuaciones de paridad y aplicadas al motor de inducción. La idea principal es aproximar, durante el marco de referencia sincrónico, el modelo no lineal del motor de inducción al modelo lineal del motor de CD, con la intención de generar un cambio significativo en los residuos obtenidos mediante ecuaciones de paridad en presencia de falla, lo cual permite la simplificación y confiabilidad de la detección de la falla. Para validar la técnica propuesta se presenta una simulación y resultados experimentales utilizando PSIM y Labview respectivamente.
Fault diagnosis of three-phase induction motor: A review
Now a days the use of Condition Monitoring of electrical machines are increasing due to its potential to reduce operating costs, enhance the reliability of operation and improve service to customers. Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in the last years. These new alternatives are characterized by an on-line and non-invasive feature, that is to say, the capacity to detect faults while the machine is working and the capacity to work sensor less. These characteristics, obtained by the new techniques, distinguish them from the traditional ones, which, in most cases, need that the machine which is being analyzed is not working to do the diagnosis. The main purpose of this article is to revise the main alternatives in the detection of faults in induction machines and compare their contributions according to the information they require for the diagnosis, the number and relevance of the faults that can be detected, the speed to anticipate a fault and the accuracy in the diagnosis. and to identify various such diagnosis techniques that can be applied for automatic condition monitoring of induction motors and can be extended easily to other electrical machines also.
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Artificial neural networks are extensively used for speed, torque estimation, and solid state drive control in both DC and AC machines. These Artificial intelligent techniques are increasingly used for condition monitoring and fault detection of machines. this paper present an overview of researches onThree phase Induction Motors Faults Detection Using Artificial Neural Network(ANN) , a general classification and brief description of the focus area for research and development in this direction are given under title of various types of faults and their detections techniques an improvement in three-phase squirrel-cage induction machine bearing, stator, eccentricity ,inter-turn, end-ring, broken-bar faults detection and diagnosis based on a neural network approach is presented .Future research directions are also highlighted.
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This paper conducts research on sensor fault diagnosis for a three-phase induction motor drive (IMD) in steady-state operation. An improving diagnostic technique based on the integration algorithm of the sinusoidal current signal is proposed for detecting and locating faulty current sensors in the induction motor drive. The IMD integrated a proposed fault detectionisolation (FDI) system is investigated for operating characteristics when sensor failures occur. The faulty sensor needs to be accurately identified and quickly isolated from the control system. Then the estimated signal will be used to replace the fault signal to retain the IMD stability. MATLAB/Simulink software will be applied to simulate the speed-torque characteristics of the IMD system as well as sensor failures occurring during operation. The performance of the proposed method will be evaluated through the accuracy and timeliness in each fault case corresponding to each sensor.
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The growing importance of power conversion systems and their dependency on the performance and reliability of static converters has motivated extensive research efforts in this field. A variety of different techniques have been applied to detect open-circuit faults in power converters. The present chapter is focusing on the techniques of detection and localization of open-circuit faults in a three phase voltage source inverter fed induction motor. A comparative study is carried out between different detection techniques: the Park current vectors and its enhancement by using the polar coordinates, the mean value of the currents, the stator current spectrum analysis and the measurement of the current drop. The aim of this comparison is to investigate the relative strengths and weaknesses of the different techniques and evaluate the performance of each detection technique studied. The comparison study focuses on the time detection, the localization ability and the hardware aspect. To validate these techniques, an experimental setup is developed in our diagnostic group laboratory which consists of a two-level voltage source inverter controlled by a DSPACE-1104, Card to generate the PWM vector control of the induction motor. The obtained simulation and experimental results illustrate well the detection feasibility of each technique as well as the benefits and merits of the performed comparative study.
Faults Classification Scheme for Three Phase Induction Motor
International Journal of System Dynamics Applications, 2014
In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear cla...
3C Tecnología_Glosas de innovación aplicadas a la pyme, 2020
In this paper, we have presented the multiple fault detection and identification system for three-phase induction motor. Fast Fourier Transform (FFT) is the most used signal processing technique that offers good frequency information but failing in providing time information and handling multiple faults identification with their occurrence time. FFT also fails to detect non-stationary condition of the signal and unable to convey sudden changes, start and end of the events, drifts and trends. To obtain simultaneous time frequency information and to deal with non-stationary signals Short Time Fourier Transform (STFT) is considered optimal technique that can clearly provide time and frequency information both. In this research work, the multiple fault detection and identification system is presented by employing Short Time Fourier Transform (STFT) signal processing technique. The proposed model is designed using current signature analysis method (CSAM) for three major faults including three phase supply imbalance, single phasing condition and breakage of rotor bars. The system is simulated in MATLAB/SIMULINK and simulation is performed based on healthy and unhealthy conditions of the motor. Comparative analysis between FFT and STFT, shows STFT as a promising approach.
Detection of stator winding faults in induction motors using three-phase current monitoring
ISA Transactions, 2011
The objective of this paper is to propose a new method for the detection of inter-turn short circuits in the stator windings of induction motors. In the previous reported methods, the supply voltage unbalance was the major difficulty, and this was solved mostly based on the sequence component impedance or current which are difficult to implement. Some other methods essentially are included in the offline methods. The proposed method is based on the motor current signature analysis and utilizes three phase current spectra to overcome the mentioned problem. Simulation results indicate that under healthy conditions, the rotor slot harmonics have the same magnitude in three phase currents, while under even 1 turn (0.3%) short circuit condition they differ from each other. Although the magnitude of these harmonics depends on the level of unbalanced voltage, they have the same magnitude in three phases in these conditions. Experiments performed under various load, fault, and supply voltage conditions validate the simulation results and demonstrate the effectiveness of the proposed technique. It is shown that the detection of resistive slight short circuits, without sensitivity to supply voltage unbalance is possible.