REVIEW ON FAULT DETECTION AND ANALYSIS TECHNIQUES FOR THREE PHASE INDUCTION MOTOR

Electrical Engineering

Induction motors, also named as asynchronous motor, are generally using equipment in manufacturing industry, petrochemical, transportation, and power systems, due to simple structure, high overload capability, reliability, and better efficiency. Different sizes of induction motors are available in industry from small to over 100K Horsepower (Hp). Compared with direct current motors, the induction motors are more rugged, cheap, and require less overall maintenance. Therefore, these are preferred choice as industrial motors by engineers. Different methods of induction motor fault diagnosis were under investigated by few researchers and different techniques have been proposed [4] for fault diagnosis. But, the most common used approach is motor Current Signature Analysis (MCSA). Several induction motor faults diagnosis techniques are based on Fast Fourier Transform (FFT) using electrical signal signature analysis. Other diagnosis methods including vibration analysis, temperature measurements, harmonic analysis of speed fluctuations, vibration monitoring, state and parameters estimation, either axial flux, acoustic noise measurement, and magnetic field analysis may diagnose through other techniques. Currently, Artificial Intelligence (AI) techniques have been combined with traditional diagnosis methods for detection of the right faults, such as Fuzzy Logic (FL), Genetic Algorithms (GA), Neural Network (NN), Bayesian classifiers, and envelope technique. In this paper, different techniques for three phase induction motor fault analysis and detection. This paper, useful for different future researcher and students in the field of three phase induction motor fault analysis and detection applications.

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