Distributed fault monitoring in manufacturing systems using concurrent discrete-event observations
Integrated Computer-Aided Engineering - Special issue: faults in automated manufacturing
Diagnosis of large active systems
Artificial Intelligence
Verification of Large State/Event Systems Using Compositionality and Dependency Analysis
TACAS '98 Proceedings of the 4th International Conference on Tools and Algorithms for Construction and Analysis of Systems
Unfoldings of Unbounded Petri Nets
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Proceedings of the 15th International Conference on Application and Theory of Petri Nets
Distributed Diagnosis for Qualitative Systems
WODES '02 Proceedings of the Sixth International Workshop on Discrete Event Systems (WODES'02)
Covering sharing trees: a compact data structure for parameterized verification
International Journal on Software Tools for Technology Transfer (STTT)
Distributed Monitoring of Concurrent and Asynchronous Systems*
Discrete Event Dynamic Systems
Exploiting independence in a decentralised and incremental approach of diagnosis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed diagnosis of discrete-event systems using Petri nets
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Automatica (Journal of IFAC)
On-line fault diagnosis in a Petri net framework
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
State estimation and fault detection using petri nets
PETRI NETS'11 Proceedings of the 32nd international conference on Applications and theory of Petri Nets
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We consider a Petri Net model of the plant. The observation is given by a subset of transitions whose occurrence is always and immediately sensed by a monitoring agent. Other transitions not in this subset are silent (unobservable). Classical on-line monitoring techniques, which are based on the estimation of the current state of the plant and the detection of the occurrence of undesirable events (faults), are not suitable for models of large systems due to high spatial complexity (exponential in the size of the entire model). In this paper we propose a method based on the explanation of plant observation. A legal trace minimally explains the observation if it includes all unobservable transitions whose firing is needed to enable the observed transitions. To do so, starting from an observable transition, using backward search techniques, a set of minimal explanations is derived, which are sufficient for detecting whether a fault event must have occurred for sure in the plant or not. The technique also allows production of a set of basis markings for the estimation of the current state of the plant. The set of all possible current markings can then be characterized as the unobservable reach of these basis markings. The computational complexity of the algorithm depends on the size of the largest connected subnet which includes only unobservable transitions. This allows monitoring of plants of any size in which there is no large unobservable subnet. We also illustrate the applicability of the method for the monitoring of a class of infinite state systems, unbounded Petri Nets with unobservable trap circuits, and we show how this can be useful for distributed implementations.