基于云粒子群优化SVM的汽轮机转子故障诊断  

Fault Diagnosis of Steam Turbine Rotor Based on CPSO-SVM

作　　者：陈长河 石志标[1] 曹丽华[2] CHEN Chang-he;SHI Zhi-biao;CAO Li-hua(School of Mechanical Engineering, Northeast Electric Power University,Jilin 132012, China;School of Energy and Power Engineering, Northeast Dianli University,Jilin 132012, China)

机构地区：[1]东北电力大学机械工程学院,吉林132012 [2]东北电力大学能源与动力工程学院,吉林132012

出　　处：《汽轮机技术》2018年第3期201-204,207,共5页Turbine Technology

基　　金：国家自然科学基金(51576036);吉林省科技发展计划项目(20100506)

摘　　要：为了提高汽轮机转子故障诊断的识别准确率和效率,提出了一种基于云粒子群算法(cloud particle swarm optimization,简称CPSO)优化支持向量机(support vector machine,简称SVM)的故障诊断方法。首先,将云理论与粒子群算法(PSO)相结合得到CPSO算法;其次,通过CPSO算法优化的SVM得到诊断模型;最后,通过ZT-3转子试验台进行汽轮机转子常见故障模拟实验,获取故障数据后进行故障识别研究。结果表明:与PSO-SVM模型相比,CPSO-SVM的诊断模型可以准确、高效地识别出故障类型,证明了该诊断方法的有效性和可行性。To improve the accuracy and efficiency of turbine rotor fault diagnosis,a new method of fault diagnosis based on the cloud particle swarm optimization algorithm（ CPSO） and support vector machine（ SVM） was introduced. Firstly,the cloud theory was introduced into the particle swarm optimization algorithm（ PSO） and the CPSO algorithm was obtained.Secondly,the optimal parameters of the SVM diagnostic model were obtained through the CPSO algorithm. Finally,the vibration data obtained from the ZT-3 steam turbine rotor simulation test rig to simulate the common faults of steam turbine rotor was analyzed. The results show that the optimized model of SVM obtained by CPSO algorithm can be used to diagnose the fault of the steam turbine rotor accurately and efficiently; Compared with the optimized SVM model,which was obtained by the particle swarm optimization algorithm,the accuracy and efficiency of CPSO-SVM model is higher. It is proved that the validity and feasibility of the fault diagnosis method for steam turbine rotor.

关 键 词：支持向量机 云粒子群算法 故障诊断 汽轮机转子 

分 类 号：TK267[动力工程及工程热物理—动力机械及工程] TH17[机械工程—机械制造及自动化]