Embedded system for the detection of brushless exciter failure
Brushless exciters are used in synchronous motors. The role of these exciter circuits is to provide field excitation as well as synchronize the speed of the rotor with the rotational speed of the moving electromagnetic field. If the exciter fails to provide the correct DC voltage the motor will not work correctly. The aim of this work is to provide a wireless system capable of monitoring the operation of a brushless synchronous motor and to provide an embedded fault detection capability. The technique used to achieve this goal utilizes a neural network to classify different working modes of the diode bridge. Two systems were developed: a wireless system, small enough to be installed inside the motor and capable of transmitting field current values to a monitoring station for display and storage. This therefore, allows real time monitoring of the motor's operation. The second system developed is based on a micro-controller and is capable of detecting open and short circuit faults that appear in the rectifying bridge of the excitation system. Implementing the Neural Network detection scheme on an embedded solution proved to be feasible. The results obtained indicate good noise tolerance, improved detection speed over previous detection systems and good cost efficiency.
Gray, Purdue University.
Computer Engineering|Engineering|Electrical engineering|Mechanical engineering
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