Abstract

This paper presents an neural network based approach to identify in real time faulty components found on industrial brushless exciters. A brushless exciter or “rotating rectifier” is a key component of a synchronous motor. Improper operation of this component can prove costly for the motor's owner. A method is based on Fourier analysis combined with the use of neural networks is presented to detect some common failures involving a three phase rotating rectifier. A laboratory setup was constructed to create fault condition data sets. These data sets were used to determine a preprocessing technique in conjunction with an appropriate neural net structure and training algorithm. Robustness of the system was tested using various levels of measurement noise to good result.

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