A Revised State Space Modelling Approach and Improved Fault Detection Using Combined Index Monitoring for Dynamic Processes

Abstract

Larimore's state space model derivation and stochastic estimation algorithm, first published in 1983, have been the adopted standard for deriving the state variables and parameters of the five (5) matrices state space model representation which continues to be applied extensively in the literature for applications ranging from controls, system identification and process monitoring. This paper presents an alternate derivation and stochastic estimation algorithm. The paper also discusses how strategic classification of the process inputs may, for some applications, facilitate the use of a simplified stochastic estimation algorithm. The alternative state space modeling approaches demonstrated better fault monitoring statistic performance for specific types of faults simulated. The canonical variate based state space modeling approaches were evaluated on a simulate CSTR process – with recycle through a heat exchanger. The results demonstrates the potential benefits to be derived from using a combined monitoring index based upon monitoring statistics derived from independent state space models for improved overall fault detecting capabilities and reliability of the fault monitoring scheme.