非线性模型在汽轮发电机组振动故障预测中的应用  

Application of Nonlinear Models to Forecast Vibration Faults of Turbo-Generator Unit

作　　者：梁平[1] 范立莉[1] 龙新峰[2] 

机构地区：[1]华南理工大学电力学院,广东广州510640 [2]华南理工大学化工与能源学院,广东广州510640

出　　处：《华南理工大学学报（自然科学版）》2006年第6期122-126,共5页Journal of South China University of Technology(Natural Science Edition)

基　　金：广东省自然科学基金资助项目(020875)

摘　　要：针对故障状态下汽轮发电机组振幅的变化呈非线性的特性,文中建立了灰色系统理论与时间序列组合预测模型、基于分形拼贴定理及分形插值的预测模型以及基于最小二乘支持向量机的预测模型,并以某电厂200MW机组的日平均振动峰-峰值作为实测数据,对所建立的3种非线性预测模型分别进行拟合,进而对其预测性能进行分析及比较,得出了适合于汽轮发电机组的故障预测模型.In order to overcome the nonlinear change of the amplitude of turbo-generator unit in malfunction, a forecasting model combining GM （Gray Model） with ARMA （Auto Regression Moving Average） , a forecasting model based on the fractal collage theorem and the fractal interpolation, and a forecasting model based on the leastsquare support vector machine are established, which are then respectively fitted with the daily average vibration peak-peak values of a 200 MW unit. At the same time, the forecasting performances of the three proposed models are analyzed and compared, with a suitable model obtained for the fault forecasting of turbo-generator units.

关 键 词：汽轮发电机 预测 时问序列 灰色系统 分形 最小二乘支持向量机 

分 类 号：O211.61[理学—概率论与数理统计]