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Distributed fault detection over sensor networks with Markovian switching topologies

journal contribution
posted on 06.12.2017, 00:00 by Xiao Hua GeXiao Hua Ge, Qing-Long HanQing-Long Han
This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via acommunication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filtersis derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

43

Issue

3-4

Start Page

305

End Page

318

Number of Pages

14

eISSN

1563-5104

ISSN

0308-1079

Location

UK

Publisher

Taylor & Francis

Language

en-aus

Peer Reviewed

Yes

Open Access

No

Acceptance Date

21/10/2013

Era Eligible

Yes

Journal

International Journal of General Systems