基于非线性主元分析的水轮机调节系统传感器故障诊断  

Fault Diagnosis for Sensors of Hydro Turbine Regulation System Based on Nonlinear Component Analysis

作　　者：刘明华[1] 南海鹏[1] 余向阳[1] 

机构地区：[1]西安理工大学水利水电学院,陕西西安710048

出　　处：《西安理工大学学报》2012年第2期204-209,共6页Journal of Xi'an University of Technology

基　　金：教育部博士点专项基金资助项目(20096118110012)

摘　　要：利用现场的运行数据,将基于输入训练神经网络的非线性主元分析(PCA)方法应用到水轮机调节系统传感器故障诊断中,讨论了基于输入训练神经网络的非线性主元分析实现方法,建立了输入训练神经网络和反向传播网络,实现了对实测数据的重构,讨论了利用平方预测误差(SPE)进行故障检测和识别的方法,并用现场实测数据对该方法进行了仿真。仿真结果表明,该方法有效且实用。A nonlinear principal component analysis methodology based on input-training neural network is proposed and applied to sensors diagnosis of hydro turbine regulating process,which is completed by establishing an input-training neural network and a backpropagation network to reconstruct sensors value.The scheme of fault detection and fault identification is discussed via the application of the squared prediction error（SPE）.Simulating results prove that this method is practically feasible with high fault recognizing rate and application value.

关 键 词：传感器 故障诊断 非线性主元分析 输入训练神经网络 水轮机调节系统 

分 类 号：TM312[电气工程—电机] TP277[自动化与计算机技术—检测技术与自动化装置]