基于动态单类随机森林的非线性过程监控方法

Nonlinear process monitoring using dynamic one-class random forest

针对高维化工过程中存在的非线性和动态特性,提出了一种基于动态单类随机森林(dynamic one-class random forest,DOCRF)的过程监控方法。对正常运行状态下的过程数据进行稀疏性分析,根据其反分布产生离群点数据。利用典型变量分析对正常数据进行相关性分析,分别将正常数据和离群点数据投影到典型变量空间,利用典型变量空间数据训练单类随机森林。基于单类随机森林模型根据待检测样本与正常数据的相似度构造监控统计量进行故障检测。在Tennessee Eastman过程的仿真结果表明,所提DOCRF方法总体优于单类支持向量机方法。

For the nonlinear and dynamic characteristics of chemical processes, a process monitoring method based on dynamic one-class random forest（DOCRF） is proposed. The sparsity of the process data under normal operating conditions is analyzed. Then, outliers are produced according to the inverse distribution of the normal data. Canonical variate analysis is used to analyze the correlation of normal data, and to project the normal data and outliers into canonical variate space. One-class random forest is trained by using the data in canonical variate space. The monitoring statistic is established according to the similarity between the test sample and the normal data in one-class random forest for fault detection. Simulation results on Tennessee Eastman process showed that the proposed DOCRF method was better than one-class support vector machine overall.