Optimization of proof test policies for safety instrumented systems using multi-objective genetic algorithms

This paper addresses the optimization of proof testing policies for safety instrumented systems with redundant parallel architectures. Testing policies include both the test interval and the test strategy. The approach addresses the requirements of the standard IEC 61508. The Average Probability of Failure on Demand, a measure of system safety integrity, Spurious Trip Rate and Lifecycle Cost are the three objectives used for the optimization. This gives as a result optimally balanced testing policies that consider the main aspects for decision making about safety system implementation: safety, reliability and cost. A new model is used for quantification of the time dependent PFD, capable of modelling explicitly different test intervals and different test strategies. It also approaches the issues of common cause failure and diagnostic coverage. The optimization is implemented using the NSGA-II genetic algorithm. The method is illustrated through an application case of a system implementing a protective function on a chemical reactor.