A DATA-BASE ORIENTED DYNAMIC METHODOLOGY FOR THE FAILURE ANALYSIS OF CLOSED-LOOP CONTROL-SYSTEMS IN PROCESS PLANTS

Dynamic methodologies are defined as those which explicitly account for the time element in system operation for failure modeling. A dynamic methodology is presented for the accurate failure analysis of closed loop control systems (CLCS) in process plants, using: (a) a data base to describe the CLCS process physics, and (b) a Markov model to describe the probabilistic evolution of the controlled variables in discrete time and discretized controlled variable state space. Both the preparation of the data base and construction of the Markov model are mechanized. The methodology is advantageous if: (a) there is an existing computer code for the fast simulation of CLCS behavior under both normal and failed component operation, (b) the failure characteristics of the CLCS for given initial conditions and over a specified time interval are of interest, and (c) several analyses need to be performed in view of uncertainties in the component failure data. The methodology is illustrated on a CLCS for maintaining the reactor vessel pressure and water levels in Boiling Water Reactors within allowed ranges following a small-break loss of coolant accident (LOCA). The results for the probability of occurrence of the Top Events (e.g. high pressure, low level) during a 60 min interval following the LOCA are compared to those predicted by the fault-tree approach using digraphs. © 1990.