Comparison-based system level fault diagnosis using game theory

We contribute in solving the well known system-level fault diagnosis problem which main objective is to identify faulty nodes based on an input syndrome that has been generated using the asymmetric comparison model. In such a model, pairs of nodes perform tasks assigned to them and the outputs of executing these tasks are compared. Based on the matching and mismatching among the nodes' outputs, the diagnosis algorithm must identify faulty nodes. In general, it is assumed that a maximum of t of these units can simultaneously fail permanently. In this paper, we describe a new game-theory-based diagnosis algorithm, which identifies the faulty nodes by maximizing the payoffs of all players (nodes). The novel approach has been implemented and evaluated using randomly generated diagnosable systems. The simulation results showed that the new game-theory-based diagnosis algorithm was able to identify all faulty situations, making it a viable addition to existing diagnosis algorithms.