Fault Diagnosis System with Natural Repair Function for Screw Oil Pump Based on Radial Basic Function Network

Considering the issues that the relationship between the fault of screw oil pump existent and fault information is a complicated and nonlinear system, and the radial basic function network (RBFNN) has the advantages of learning speed rapidly and fine ability of function approaching and model classify, a fault diagnosis system with natural repair function for screw oil pump based on RBFNN is presented in this paper. We construct the structure of radial basic function network that used for the fault diagnosis of screw oil pump, and adopt the K-Nearest Neighbor algorithm to train the network. With the ability of strong self-learning and function approach and fast convergence rate of radial basic function network, the diagnosis system can truly diagnose the fault of screw oil pump by learning the fault information. The real diagnosis results show that this system is feasible and effective.