Study of Fault Diagnosis in Brushless Machines Based on Artificial Immune Algorithm

It is describes a kind of artificial immune algorithm (AIA), which is based on the nature immune system in this paper, and the basic principle of the immune system is presented. And the negative-selection algorithm is inspired by the negative-selection mechanism of the nature immune system, and it is can detect any tiny change in an actual system, so it can be used in the state monitoring and fault diagnosis. In order to increase effectively the operation reliability of the brushless machines, a new detection method has been presented to the faults diagnosis of rotating rotor rectifiers in generators with brushless excitation systems, which is based on the negative-selection algorithm. When any rotating rotor rectifier has some faults, such as one bridge arm breaking, then the rectifiers are under abnormal operating mode, and the current waveform is changed, so the signal waveform monitored is unusual. Different fault has different signal waveform, and the signal waveform can be also analyzed by taking advantage of the negative-selection algorithm, then the fault type can be identified. The development trends of the application of the negative-selection algorithm for fault diagnosis are also introduced