This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between "healthy" and "faulty" machine.
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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