Multi-decision diagnosis: decentralized architectures cooperating for diagnosing the presence of faults in discrete event systems

This article deals with decentralized diagnosis, where a set of diagnosers cooperate for detecting faults in a discrete event system. We propose a new framework, called multi-decision diagnosis, whose basic principle consists in using several decentralized diagnosis architectures working in parallel. We first present a generic form of multi-decision diagnosis, where several decentralized diagnosis architectures work in parallel and combine their global decisions disjunctively or conjunctively. We then study in more detail the inference-based multi-decision diagnosis, that is, in the case where each of the decentralized architectures in parallel is based on the inference-based framework. We develop a method that checks if a given specification is diagnosable under the inference-based multi-decision architecture. We also show that with our method, the worst-case computational complexity for checking codiagnosability for our inference-based multi-decision architecture is in the same order of complexity as checking codiagnosability for the inference-based architecture designed by Kumar and Takai. In fact, multi-decision diagnosis is fundamentally undecidable and we have formulated a decidable variant of it. Multi-decision diagnosis is formally based on language decomposition, but it is worth noting that our objective is not to answer the existential question of language decomposition in the general case. Our objective is rather to propose a decentralized diagnosis architecture that generalizes the decidable existing ones.