Study on the application of the hierarchical fuzzy neural network in the fault diagnosis of the asynchronous motor

This thesis presents a fault diagnosis method based on the low, middle and high level fuzzy neural networks for the breakdown asynchronous motor according to the complex corresponding relations between the motor´s fault symptoms and the fault causes. This can implement the fuzzy diagnosis for the motor fault. The thesis puts emphasis on the structure models of the new type hierarchical fuzzy neural network and the relative learning algorithms. And it also introduces the simulation training of the hierarchical fuzzy neural network based on the models and algorithms. At last, the experimental results show that this diagnosis method can effectively classify the single fault samples and the multi fault samples of the motor and this not only can raise the accurateness rate of the diagnosis, but it also possesses a good applicable value in engineering.