Fault Diagnosis of Steam Turbine-Generator Sets Using Evolutionary Based Support Vector Machine

This paper presents particle swarm optimization (PSO)-based support vector machine (SVM) to extract the optimal support vector from database for vibration fault diagnosis of steam turbine-generator sets (STGS). In this paper, the SVM is used to construct the vibration fault diagnosis model and the proposed PSO is then adopted to determine automatically the optimal parameters in the SVM. Test results demonstrated that the proposed PSO-based SVM method can achieve greater diagnostic accuracy than the SVM method. Thus, this paper has demonstrated the feasibility of applying the proposed PSO-based SVM method to the practical vibration fault diagnosis of the STGS.