Steam Turbine Vibration Fault Diagnosis Based on ParticleSwarm Optimization Clustering
LIU Fu-rong1;WANG Chang-hong1; GAO Xiao-Zhi2
（1 Harbin Power Vocational Technollgy College,Harbin 150030 China；2 Department of Electrical Engineering, Helsinki University of Technology, Otakaari 5 A, 02150 Espoo, Finland ）
Abstract：A novel fuzzy clustering algorithm: PFCM was proposed based on fusion of the particle swarm optimization (PSO) and Fuzzy C-means clustering (FCM). The conventional FCM has the two drawbacks of sensitivity to initialization and easily being trapped into local optima, due to the gradient descent approach used. With the features of global optimization and fast convergence, the hybrid algorithm presented can overcome these shortcomings and yield the optimal clustering performance. The new data clustering technique provided was also applied in the vibration fault diagnosis of steam turbine. Computer simulations demonstrate that compared with FCM, the proposed PFCM has a superior fault diagnosis capability.
2010, Vol. 29 
