西安理工大学学报2013，Vol.29Issue(2)：172-175,4.
基于DPSO优化支持向量机的水轮机组振动故障诊断
Hydraulic Generating Vibration Faults Diagnosis by Support Vector Machine Based on Particle Swarm Optimization
摘要
Abstract
According to the basic PSO algorithm,searching for optimum parameters of support vector machine in the late stage is easy to fall into local optimum,and further affects support vector machine in hydraulic turbine vibration fault diagnosis correct rate.With an aim at this problem,the dynamic particle swarm algorithm (DPSO) is selected to optimize the support vector machine.The hydraulic turbine fault feature vector is input into the optimized support vector machine fault diagnosis.The simulation results show that DPSO optimized SVM can find the global optimal solution,thereby having good classification accuracy.In the hydraulic turbine vibration fault diagnosis compared to the traditional PSO optimized support vector machine has higher diagnostic accuracy.关键词
水轮机/振动故障诊断/动态粒子群算法/支持向量机Key words
hydraulic turbine/ vibration faults diagnosis / PSO/ SVM分类
能源科技引用本文复制引用
张欣伟,陈文献,张锋利..基于DPSO优化支持向量机的水轮机组振动故障诊断[J].西安理工大学学报,2013,29(2):172-175,4.基金项目
国家自然科学基金资助项目(51279161). （51279161）
