Robust fault detection of dynamic systems via genetic algorithms

This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.