Design of light weight exact discrete event system diagnosers using measurement limitation: case study of electronic fuel injection system

Abstract

ABSTRACTEmploying a state-based Discrete Event System (DES) modelling framework, this paper proposes a new fault diagnosis approach called measurement limitation-based abstract DES diagnosis (MLAD), which attempts to reduce state space complexity of the diagnosis process while simultaneously preserving full diagnosability. The MLAD approach carefully applies a set of distinct measurement limitation operations on the state variables of the original DES model based on fault compartmentalisation to obtain separate behaviourally abstracted DES models and corresponding abstract diagnosers with far lower state spaces. The set of measurement limitation operations are so designed that although, any single abstract diagnoser may compromise diagnosability in seclusion, the additive combination of all diagnosers running in parallel always ensures complete diagnosability. Effective measurement limitation also ensures that the combined state space of the abstract diagnosers is much lower than that of the single full diagnoser that may be derived from the original DES model. As a case study, we have employed MLAD to incorporate failure diagnosability in a practical electronic fuel injection system. Evaluations on standard practical benchmarks show that MLAD achieves significant reduction in state space as compared to conventional monolithic full diagnosis approaches.