Minimization of sensor activation in decentralized fault diagnosis of discrete event systems

We investigate the problem of dynamic sensor activation for decentralized fault diagnosis in partially-observed discrete-event systems, where the system is monitored by a set of agents. The sensors of each agent can be turned on/off online dynamically according to a sensor activation policy. The goal is to find a language-based minimal sensor activation policy for each agent such that the agents can still diagnose, as a team, all fault occurrences. A novel approach to solve this problem is proposed. We adopt a person-by-person approach to decompose this decentralized minimization problem into two centralized constrained minimization problems. Each centralized constrained minimization problem is then reduced to a fully centralized sensor activation problem that is solved effectively by an algorithm presented in a related contribution. The solution obtained is provably person-by-person minimal with respect to the system language.