Hydraulic Turbines Vibration Fault Diagnosis by RBF Neural Network Based on Particle Swarm Optimization

For the system of vibration faults diagnosis of hydraulic turbines, the deficiency of generalization ability using single BP Network is analyzed and a radial basis function (RBF) neural network algorithm based on particle swarm optimization (PSO) is presented. It has advantage of being easy to realize, simple operation and profound intelligence background. The parameters and connection weight are optimized by the algorithm. The diagnostic results of the instance show that it has better classifying results, higher precision, faster convergence and it provides a new way in the field of fault diagnosis of hydraulic turbines.