A bridged diagnostic method for the monitoring of polymorphic discrete-event systems.
Diagnosis of discrete-event systems (DESs) is a challenging problem that has been tackled both by automatic control and artificial intelligence communities. The relevant approaches share similarities, including modeling by automata, compositional modeling, and model-based reasoning. This paper aims to bridge two complementary approaches from these communities, namely, the diagnoser approach and the active system approach, respectively. The more significant shortcomings of such approaches are, on the one side, the need for the generation of the global system model and, on the other, the lack of monitoring capabilities. The former makes the application of the diagnoser approach prohibitive in real contexts, where the system model is too large to be generated, even offline. The latter requires the completion of the system observation before starting the diagnostic task, thereby, making the monitoring of the system. impossible. The bridged diagnostic method subsumes, to a large extent on the peculiarities of the two approaches and is capable of coping with an extended class of DESs that integrate both synchronous and asynchronous behavior. The bridge is built by extending the active system approach by means of several enhanced techniques, which eventually, allow the efficient monitoring of polymorphic DESs. Upon the occurrence of each system message, two pieces of diagnostic information are generated, namely, the snapshot and historic diagnostic sets. While the former accounts for the faults pertinent to the newly generated message only, the latter is based on the whole sequence of messages yielded by the system during operation.
Lamperti
Gianfranco
G
Dipartimento di Elettronica per l'Automazione, Università degli Studi di Brescia, 25123 Brescia, Italy. lamperti@ing.unibs.it
Zanella
Marina
M
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods*; D033183:Interdisciplinary Communication; D008954:Models, Biological; D012106:Research / Q000639:trends; D012107:Research Design; D012815:Signal Processing, Computer-Assisted; D018511:Systems Integration