摘要
提出以劣化度作为汽轮机运行状态的评价指标,针对汽轮机不同类型的监测信号,将劣化度分为4类,分别给出其计算方法;对劣化度时间序列进行数据预处理,通过分析预测模型识别、模型定阶、模型参数估计、模型检验与优化方法,建立起了汽轮机运行状态的ARMA预测模型。以某电厂600MW机组3号轴承振动数据为例,实现了机组运行状态预测。
In this paper,the deterioration degree is taken as the operation evaluation index of the steam turbine.According to the different types of monitoring signals of the unit,the deterioration degree is divided into four categories,and the calculation methods are given respectively.The time series of the deterioration degree is preprocessed,and the ARMA prediction model of steam turbine is established by analyzing methods of the prediction model identification,model order determination,model parameter estimation,model inspection and optimization.Taking the vibration data of No.3 bearing of a 600MW unit as an example,the condition prediction of the unit is realized.
作者
李晓波
焦晓峰
贾斌
何成兵
LI Xiao-bo;JIAO Xiao-feng;JIA Bin;HE Cheng-bing(Inner Mongolia Power Research Institute,Hohhot 010020,China;North China Electric Power University,Beijing 102206,China)
出处
《汽轮机技术》
北大核心
2020年第6期447-450,共4页
Turbine Technology
关键词
汽轮机
劣化度
状态预测
ARMA
评价指标
steam turbine
deterioration degree
condition prediction
ARMA
evaluation index
