Vibration Fault Diagnosis of Rotating Machinery Using PSO-Based Radial Basis Function Network

Abstract

A particle swarm optimization-based radial basis function network (PSO-RBFN) is presented to diagnose vibration faults of steam turbine-generator sets (STGS) in a power plant. The proposed PSO algorithm is used to automatically tune the control parameters of the RBFN. The test results demonstrate that the proposed PSO-RBFN has a higher diagnostic accuracy than the RBFN and multilayer perceptron network (MLPN) trained by error back-propagation algorithm. Moreover, this paper has demonstrated that the proposed PSO-RBFN can be as a reliable tool for vibration fault diagnosis of STGS.