Automatic synthesis and compression of cardiological knowledge
Machine intelligence 11
On Communicating Finite-State Machines
Journal of the ACM (JACM)
Automata For Modeling Real-Time Systems
ICALP '90 Proceedings of the 17th International Colloquium on Automata, Languages and Programming
Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques
Artificial Intelligence
A diagnostic environment for automaton networks
Software—Practice & Experience
Diagnosis of Discrete Event Systems Using Decentralized Architectures
Discrete Event Dynamic Systems
Diagnosability Analysis of a Class of Hierarchical State Machines
Discrete Event Dynamic Systems
Model-Based Diagnosis of Discrete Event Systems with an Incomplete System Model
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Observation-Subsumption Checking in Similarity-Based Diagnosis of Discrete-Event Systems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques
Artificial Intelligence
Diagnosability of fuzzy discrete-event systems: a fuzzy approach
IEEE Transactions on Fuzzy Systems
A Decentralised Symbolic Diagnosis Approach
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Active fault tolerant control of discrete event systems using online diagnostics
Automatica (Journal of IFAC)
Fault tolerant supervisory for discrete event systems based on event observer
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
First steps towards incremental diagnosis of discrete-event systems
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Safe diagnosability for fault-tolerant supervision of discrete-event systems
Automatica (Journal of IFAC)
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Detection and isolation of failures in large and complex systems such as telecommunication networks are crucial and challenging tasks. The problem considered here is that of diagnosing the largest French packet switching network. The challenge is to be as efficient as the existing expert system while providing greater generality and flexibility with respect to technological and reconfiguration changes in the network. The network is made up of interconnected components each of which can send, receive and transmit messages via its ports. The problem we are faced with is to follow the evolution of the network on the basis of the stream of time-stamped alarms which arrive at the supervision center. We have decided to use model-based techniques which are recognized to be more adapted to evolutive systems than expertise-based approaches are. This paper starts with a description of how we model the global behavior of this discrete-event system by using communicating finite state machines. It goes on to explain how this model is used for analyzing the stream of alarms and diagnosing the network. Our work is based on the diagnoser approach proposed by Sampath et al. (1995). Starting from a model of the network adapted to simulate faults, this approach transforms it into a finite state automaton, called a diagnoser, in order to analyze the stream of alarms. The approach described in Sampath et al. (1995; 1996) proved to be grounded on certain basic hypotheses which were too restrictive for our application. This paper extends Sampath’s proposal to communicating finite state machines. The difficulties we had to cope with are outlined and the way we overcome them is presented. A major difficulty is the huge size of the global model of the system. To solve this problem we take advantage of the hierarchical structure of the network and rely on a generic model of the system for building a generic diagnoser.