Statistical diagnostic in the closed loop autoregressive model

Abstract

In dynamically control field, the effectiveness of closed loop control is based on good knowledge of system parameters; it is then important to detect parameters changes, chiefly in off-line estimation field. According to the physical structure of plants represented by closed loop autoregressive models the system parameters may be divided into four classes: level and dynamic of transform system, level and dynamic of control system. This gives rise to four kinds of parameter ruptures, where rupture means step-change from one state to another. In the present paper the on-line rupture detection and classification problem is solved by means of a multiple decision procedure which enables the user to localize the rupture area.