Fault diagnosis of manufacturing systems using finite state automata

.This chapter presents the salient features of a general methodology for fault diagnosis in partiallyobservedfinite-state automata and its application to automated manufacturing systems. The system ofinterest is modeled as a set of interacting automata coupled by common events. The total event setcomprises observable and unobservable events, reflecting those events that can, or cannot, be perceivedby the sensors attached to the manufacturing system. Fault events are inherently unobservable and thediagnostic task is to infer their occurrence from the sequences of observable events and the system model.On-line diagnosis is performed using diagnoser automata, which are constructed from the system modeleither off-line or on-the-fly. The analysis of the diagnosability properties of the system is done off-lineusing verifier automata, which are also constructed from the system model. The algorithms presentedare illustrated with relevant examples. The chapter concludes with a discussion of sensor selection fordiagnosability and of cooperative diagnosis for systems with decentralized information.