Abstract-This paper considers the problem of designing distributed fault diagnosis algorithms for dynamic systems using sensor networks. A network of distributed estimation agents is designed where a bank of local Kalman filters is embedded into each sensor. The diagnosis decision is performed by a distributed hypothesis testing method that relies on a belief consensus algorithm. Under certain assumptions, both the distributed estimation and the diagnosis algorithms are derived from their centralized counterparts thanks to dynamic average-consensus techniques. Simulation results are provided to demonstrate the effectiveness of the proposed architecture and algorithm.
The paper REF considers the problem of designing fault diagnosis algorithms for dynamic systems using sensor networks.
Distributed Fault Diagnosis using Sensor Networks and Consensus-based Filters
Proceedings of the 45th IEEE Conference on Decision and Control
Proceedings of the 45th IEEE Conference on Decision and Control
Computer Science