STATIC AIR-GAP ECCENTRICITY FAULT DETECTION OF INDUCTION MOTOR USING ARTIFICIAL NEURAL NETWORK (ANN)

KHALED MOHAMMED BIR GAMAL; SUPRIYA P. PANDA; M. V. RAMANA MURTHY

doi:10.29121/granthaalayah.v8.i8.2020.1146

Authors

Khaled Mohammed Bir Gamal Research Scholar, Department of Electrical & Electronics Engineering, Manav Rachna International Institute for Research and Studies, Faridabad, India
Supriya P. Panda Professor, Department of Computer Science and Engineering, Manav Rachna International Institute for Research and Studies, Faridabad, India
M. V. Ramana Murthy Professor(R), Department of Mathematics & Computer Science, Osmania University, Hyderabad, MGIT(P), Hyderabad, India

DOI:

https://doi.org/10.29121/granthaalayah.v8.i8.2020.1146

Keywords:

Induction Motor, Air Gap Eccentricity Fault, Motor Current Signature Analysis (MCSA), Artificial Neural Network (ANN), Matlabsoftware

Abstract [English]

Induction motor plays an important role in the industrial, commercial and residential industries, owing to its immense advantages over the opposite types of motors. Such motors have to operate under different operating conditions that cause engine degradation leading to fault occurrences. There are numerous fault detection techniques available. There are numerous fault detection techniques available. The technique used in this paper to prove the effect of static air gap eccentricity on behaving or performing of the three-phase induction motor is the artificial neural network (ANN) as ANN depends on detecting the fault on the amplitude of positive and negative harmonics of frequencies. In this paper, we used two motors to achieve real malfunctions and to get the required data and for three different load tests. In this paper, we adopted MCSA to detect the fault based on the stator current. The ANN training algorithm used in this paper is back propagation and feed forward. The inputs of ANN are the speed and the amplitudes of the positive and the negative harmonics, and the type of fault is the output. To distinguish between healthy and faulty motor, the input data of ANN are well-trained via experiments test. The methodology applied in this paper was MATLAB and present how we can distinguish between healthy and faulty motor.

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