Decentralized Diagnosis based on Boolean Discrete Event Models: Application on Manufacturing Systems

This paper proposes a decentralized structure based on a set of local diagnosers to monitor discrete event manufacturing systems with discrete sensors and actuators. The occurrence of any fault entailing the violation of the desired behaviour must be detected and isolated by at least one local diagnoser using its own local observation about the system execution. These local diagnosers infer the fault’s occurrence using event sequences, time delays between correlated events and state conditions, characterized by sensors readings and commands issued by the controller. A very limited communication among diagnosers is permitted through a simple coordinator based on a set of rules. The goal is to solve the problem of diagnosis’s decision ambiguity due to the partial observation of diagnosers. An adapted codiagnosability notion is formally defined in order to ensure that the set of local diagnosers is able to diagnose all faults entailing the violation of the desired behavior within a bounded delay. We show that the construction of these diagnosers is polynomial in the size of the system. An example is used to illustrate the proposed approach.