Performance Assessment for Multivariate Alarm Systems Based on Markov Model

Alarm systems are essential in safe operation of industrial plants. Since many process variables are interacting with each other, so in this paper, a new method are introduced to design and analysis of a multivariate alarm system. In this method, the alarm system is designed base on joint indices. The Joint FAR and Joint MAR are defined for a m-variable alarm system thanks to multivariate Markov scheme. In proposed method, the alarm joint indices are defined by solving a Linear Programing (LP) optimization problem. By defining joint indices, tuning of the alarm parameters (like, threshold and etc.) can be done by these indices instead of correlation analysis. In this paper, penalty scenario and Genetic algorithm are used for alarm generation, and parameter optimization in Tennessee Eastman (TE) Process. The results of proposed method are compared with other methods.