STUDY ON SELF-LEARNING VIBRATION FAULT DIAGNOSIS SYSTEM OF TURBOGENERATOR UNIT
Ge Zhi · Proceedings of the CSEE · 2000
For a given diagnosis system, its diagnosis ability lies on the knowledge capacity. It is incapable to detect a new fault condition if no priori knowledge is given. We divide the conventional networks into several sub nets, which is responsible for one specific fault class. The vibration series has obvious fractal feature. It can reflect the essential characteristics of new fault. When the new fault is taken on, a new sub net is increased and trained with the sample. If other samples are identified as this new class according to proximity, it has been verified experimentally these fractal dimensions of one class are distributed approximately around a definite value that can represents the dimension of the standard sample for the novel fault. Based on non linear theorem, the approach of identifying new fault and self learning for diagnosing is put forward.