基于特征子空间的系统性能监控与工况识别

In the operation and control of chemical processes, automatic data logging systems generate large volumes of data. It is important for supervising daily operation how to make use of the valuable information about normal and abnormal operation, significant disturbance and changes in operational and control strategies. In this paper, principal component analysis(PCA)is clarified from the view of space, and every different subspace represents different operational mode and process performance. Based on that, the distance between two subspaces is calculated to evaluate the difference between them. The method is illustrated by a case study of a fluid catalytic cracking unit(FCCU) reactor-regenerator system.