The Fault Modeling of Induction Motor Based on Fusion of Genetic Algorithm And Neural Networks

摘要

The study of early intelligent obstacle diagnosis for large-sized mechanical equipment is of momentous social significance and far-reaching economic importance. The equipment directly influences production safety for enterprises, and concern economic efficiencies. If the induction motor, as one of the most important equipments, stopped production for one hour, more than 300,000 RMB would be lost. So it is very important for the induction motor to guarantee non-failure work time and the whole cutting process without fault. The early-term fault and in-time diagnosis, however, is the most basic prerequisite for the amount ahead of schedule of maintenance. It isn’t an easy matter for the induce motor to fulfill the intelligent diagnosis of early fault. It needs us to test the real-time work features and know the deep-level knowledge about its model. We have proposed fault modeling of induction motor based on fusion of genetic algorithm and neural networks.