Fault diagnosis of manufacturing systems using finite state machines

This chapter presents the salient features of a general methodology for fault diagnosis in partially observed finite-state automata and its application to automated manufacturing systems. The system of interest is modeled as a set of interacting automata coupled by common events. The total event set comprises observable and unobservable events, reflecting the set of sensors attached to the manufacturing system. Fault events are inherently unobservable and the diagnostic task is to infer their occurrence from the sequences of observable events and the system model. On-line diagnosis is performed using diagnoser automata, that are constructed from the system model. The analysis of the diagnosability properties of the system is done off-line using verifier automata, also constructed from the system model. The algorithms presented are illustrated with relevant examples. The chapter concludes with a discussion of sensor selection for diagnosability and of cooperative diagnosis for systems with decentralized information.