Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Fault Diagnosis for Timed Automata
FTRTFT '02 Proceedings of the 7th International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems: Co-sponsored by IFIP WG 2.2
WODES '02 Proceedings of the Sixth International Workshop on Discrete Event Systems (WODES'02)
Diagnosability of Discrete Event Systems with Modular Structure
Discrete Event Dynamic Systems
Safe Supervisory Control Under Observability Failure
Discrete Event Dynamic Systems
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Diagnosis of Discrete Event Systems Using Decentralized Architectures
Discrete Event Dynamic Systems
An Incremental Approach for Pattern Diagnosability in Distributed Discrete Event Systems
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Robust codiagnosability of discrete event systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Sensor and actuator fault diagnosis of systems with discrete inputs and outputs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decentralized failure diagnosis of discrete event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Verification of robust diagnosability for partially observed discrete event systems
Automatica (Journal of IFAC)
Robust diagnosis of discrete event systems against intermittent loss of observations
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
We consider the problem of diagnosing the occurrence of a certain unobservable event of interest, the fault event, in the operation of a partially-observed discrete-event system subject to permanent loss of observations modeled by a finite-state automaton. Specifically, it is assumed that certain sensors for events that would a priori be observable may fail at the outset, thereby resulting in a loss of observable events; the diagnostic engine is not directly aware of such sensor failures. We explore a previous definition of robust diagnosability of a given fault event despite the possibility of permanent (and unknown a priori) loss of observations and present a polynomial time verification algorithm to verify robust diagnosability and a methodology to perform online diagnosis in this scenario using a set of partial diagnosers.