DISTURBANCE DETECTION AND ISOLATION BY DYNAMIC PRINCIPAL COMPONENT ANALYSIS

In this paper we extend previous work by ourselves and other researche rs in the use of principal component analysis (PCA) for statistical pr ocess control in chemical processes. PCA has been used by several auth ors to develop techniques to monitor chemical processes and detect the presence of disturbances [1-5]. In past work, we have developed metho ds which not only detect disturbances, but isolate the sources of the disturbances [4]. The approach was based on static PCA models, T-2 and Q charts [6], and a model bank of possible disturbances. In this pape r we use a well-known 'time lag shift' method to include dynamic behav ior in the PCA model. The proposed dynamic PCA model development proce dure is desirable due to its simplicity of construction, and is not me ant to replace the many well-known and more elegant procedures used in model identification. While dynamic linear model identification, and time lag shift are well known methods in model building, this is the f irst application we are aware of in the area of statistical process mo nitoring. Extensive testing on the Tennessee Eastman process simulatio n [7] demonstrates the effectiveness of the proposed methodology.