Fault Detection of Brushless Exciter within Synchronous Motors
Synchronous machines are useful in small and large applications such as compressors, fans, pumps, power generators, and many other applications where precise speed control is necessary. Modern brushless excitation synchronous motors utilize electronic components to start the motor as well as lock the rotor into a synchronous speed. The electrical component of a brushless exciter contains a full-wave rectifier, which may malfunction due to a short circuit or open circuit failure of a diode. These failures are not usually detected unless the machine is inspected by a certified technician offline. The best way to prevent major damage to a machine is the have frequent maintenance, which would consist of shutting down the motor and inspecting the electronic components. Having vital machinery shutdowns routinely can reduce production time and is a reason that frequent maintenance is not a common practice in industry. The purpose of this thesis is to extend the work done by previous students in designing and testing the feasibility of using artificial neural networks as a method for detecting faults occurring within the brushless exciter’s diode rectifier of a synchronous motor. The main objectives that this thesis aims to achieve are to create a real-time working fault detection system on a Simulink platform allowing for compilation of the fault detection system to a variety of hardware platforms including microcontrollers. Secondly, the use of ANSYS Maxwell/Simplorer software configured with specific synchronous machine and mechanical load parameters will be investigated as a means to generate and provide the data needed to train an artificial neural network to detect the rectifier failure modes. This would eliminate the current need to first obtain experimental data from the synchronous machine prior to neural network training and ultimate installation of a real-time system on the motor.
Gray, Purdue University.
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