融合分层分块信息的轧制过程运行状态评估方法及应用

工业过程运行状态评估方法是对过程当前运行状态进行合理的评价,为工业过程的安全、高效运行提供有益的指导.带钢轧制过程具有流程长且系统层级多等特点,而传统的运行状态评估往往采用集中式的评估方法,难以对轧制过程全流程的运行状态进行合理的评估.针对此问题,提出一种融合分层分块信息的轧制过程运行状态评估方法.采用一种多层级分块的评估策略,将全流程分为若干个层级和子块,提高评估结果的可解释性.提出一种联合核主成分分析(kernel principal component analysis,KPCA)和t-分布随机邻域嵌入算法(t-distributed stochastic neighbor embedding,t-SNE)的特征提取方法,并行地提取全局和局部的特征信息.进而,针对传统支持向量机(support vector machine,SVM)输出结果为硬判型输出,将SVM的输出结果映射为后验概率,并通过D-S证据理论融合多个层级的运行状态评估结果,从而实现决策层面的信息融合,提高评估结果的准确性.最后,将所提出方法应用于实际带钢轧制过程,与各类传统的方法相比评估准确率提高近18%.

An operating performance assessment method for tolling processes via integrating hierarchical and block information

Operating performance assessment methods are performed to evaluate the operating performance of industrial processes so as to provide helpful guidance for safe and high-efficient operations. The rolling processes are always featured with plant-wide characteristics and equipped with hierarchical information systems, while traditional assessment methods are centralized, which, thus, are less efficient. To solve this problem, this paper proposes a method that integrates hierarchical and block information. Firstly, a hierarchical strategy is adopted, and the entire process are partitioned into several levels and sub-blocks such that the interpretability of the assessment results can be improved. Then, in order to extract comprehensive operating state features, a combination of kernel principal component analysis and t-distributed stochastic neighbor embedding-based method is proposed, which can capture both global and local features in a parallel manner. In addition, to overcome the hard decision-making results caused by support vector machines(SVMs), this paper transfers the results of the SVM to calculate the posterior probability, and makes use of the D-S evidence theory to fuse the decision-making process. Finally, the proposed method is applied to a real hot rolling mill process, the results show that it improves at least 18% assessment rate compared with several traditional methods.