基于不确定信息的系统建模与状态预测方法

A System Modeling and Condition Forecasting Approach with Uncertain Information

针对工业过程中广泛存在的信息不确定性、信息传递的时滞特性以及过程本身的非线性和动态特性,研究了基于不确定数据信息的生产过程系统级建模与过程变量的状态预测方法;提出基于小波互信息的信息传递时延估计方法,建立了概率时延符号有向图模型;采用组合预测方法,实现生产过程中关键变量实时状态的准确预测。在空分过程中进行了应用研究,初步结果表明该方法具有良好的预测精度、实用有效,具有应用推广前景。

Considering the uncertainty of information and the nonlinearity and dynamics in large-scale complex industrial processes, a system-level process modeling and condition forecasting approach is developed. A time delay estimation method is proposed based on the combination of wavelet transform and mutual information theory. Then, a probabilistic time-delayed signed digraph model is built, based on which, a combined prediction method is adopted to estimate the future status of key process variables. The proposed approach is verified on an air separation process. Preliminary results show that the approach is effective and has satisfying prediction performance. This study is expected to have a good prospect in industrial application.