摘要
传统的基于主元分析(PCA)的过程监控技术可以对工业过程当前的状况进行监控,但难以预测系统未来的运行情况。本文在PCA监控方法的基础上建立预测模型,首先根据历史数据建立PCA的综合监控统计量模型,其次结合k邻近(k-NN)的最小二乘支持向量机(LSSVM)和灰色理论(GM(1,1))技术建立在线组合预测模型,实现对工业过程运行状态的预测。利用组合模型对一个多变量动态过程实例进行仿真,将预测效果与k-NN的LSSVM以及GM(1,1)各自单独的预测效果作比较,验证了所提方法在长期预测方面的准确性,表明基于组合模型的PCA预测监控方法特别适用于对缓慢漂移故障的长期预测。
Traditional principal component analysis(PCA)-based process monitoring technologies focus on the current states of industrial processes rather than their future behavior.Motivated by this observation,this paper introduces a PCA-based predictive model which implements prediction of the operating states of industrial processes.Monitoring models of a PCA combined statistical index are first established based on normal historical data for the process.A k-NN least squares support vector machine(LSSVM) combined with GM(1,1) is then adopted to establish a on-line combinatorial prediction model,which allows forecasting of future states of the industrial process.Simulations on a multi-variables dynamic process demonstrated that the proposed method is more accurate than either k-NN based LSSVM or GM(1,1) in long-term prediction monitoring.The results of this work prove that PCA based on hybrid models is especially suitable for the long-term prediction of slowly drifting faults.
出处
《北京化工大学学报（自然科学版）》
CAS
CSCD
北大核心
2014年第2期104-108,共5页
Journal of Beijing University of Chemical Technology(Natural Science Edition)
关键词
主元分析
长期预测
K-NN
最小二乘支持向量机
GM(1
1)
principal component analysis(PCA)
long-term prediction
k-NN
least squares support vector machine(LSSVM)
GM(1,1)
