An Incremental Approach for Pattern Diagnosability in Distributed Discrete Event Systems

Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Recent work on diagnosability has generalized fault event case to pattern case, which can describe more general objectives for diagnosis problem, but based on global model and global twin plant construction. In this paper, we propose an original framework to solve pattern diagnosability in a distributed way to avoid calculating global objects. We first show how to incrementally accomplish pattern recognition without building global model by propagating only diagnosability relative information between components. Then an efficient way to construct pattern verifier is proposed, which is inspired from the classical twin plant method but with smaller state space, to search for partial critical paths, whose global consistency is subsequently checked. Meanwhile we prove that the result obtained from our distributed approach is on an equality with that from the centralized one but the evaluation result shows that our search state space exploited is only a small subpart of the global twin plant, whose construction is unavoidable in the centralized approach.