New M.E. Thesis Submitted from EE Student

COMPARATIVE ANALYSIS OF FUZZY LOGIC AND NEURO FUZZY LOGIC FOR CONDITION MONITORING OF THREE PHASE INDUCTION MOTOR By Preeti,Electrical

Abstract
In this thesis condition monitoring of three phase induction motor is judged by applying both Fuzzy Logic and Neural Fuzzy logic on the stator currents. Then comparison of both techniques is done. Electric machinery can significantly reduce the cost of maintenance and the risk of unexpected failures by allowing the early detection of potentially catastrophic faults. Fuzzy systems and Neural Networks have been recognized as attractive alternatives to the classical control schemes for the low-cost and facilitated design of the control laws of partially known, nonlinear and complex processes. Neural networks and fuzzy systems have been extensively applied to many problems including system identification, time series prediction, classification and control. However, both these approaches have serious limitations. By the application of novel neuro-fuzzy techniques these limitations can be circumvented. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. The artificial neural network, however, does not provide any heuristic knowledge of the fault detection procedure. One of the most widely used techniques for obtaining information on the health state of induction motors is based on the processing of stator line current. Typically, in the motor fault diagnosis process, sensors are used to collect time domain current signals. The diagnostic expert then uses both time domain and frequency domain signals to study the motor condition and determines what faults are present.


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