Efficient on-line failure identification for discrete-event systems

This chapter addresses the problem of diagnosing complex embedded discrete-event systems. Given a flow of observations from the system, the goal is to explain these observations by identifying possible failures. Approaches that efficiently compute the possible failures, like the classical diagnoser approach, suffer from large space complexity and hence cause difficulties in embedding diagnostic activities. This chapter presents a method that dramatically reduces the size of the classical diagnoser and keeps its efficiency. The chapter describes an event-based diagnose approach in which observations and failures are modeled as events. The states of the system are not related to the failures. The aim is to efficiently compute an even smaller finite state machine than before that directly maps observations to the failure sets of the system. © 2007