In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In\nindustrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault\nfrequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors.The misalignment\nexperiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor\nfault diagnosis process. Furthermore,manufacturing and operating noisy environment could also disturb themotor fault diagnosis\nprocess. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault\ntype when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on\nthe different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed\ntechnique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number\nof feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through\nclassification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.
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