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Centralized Bayesian reliability modelling with sensor networks

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Listed:
  • K. Dedecius
  • V. Sečkárová

Abstract

The article concerns reliability estimation in modern dynamic systems. It introduces a novel approach, exploiting a network of several independent spatially distributed sensors, actively probing the monitored system. A dedicated network element – the fusion centre – is then responsible for processing the information provided by sensors and evaluation of final reliability estimate. On the base of computational abilities of sensors, we propose two conceptually different reliability estimation scenarios: (1) the computationally cheaper dummy sensors scenario, in which the sensors send raw data to the fusion centre; and (2) the smart sensors scenario, when the data are processed locally by sensors, and the fusion centre subsequently merges their resulting information. The local processing allows to obtain ‘low-level’ reliability estimate from a particular sensor, which is of interest in large networks with communication constraints. In both cases, the emphasis is put on recursiveness, adaptivity and robustness of solutions. The Bayesian paradigm was adopted for consistent information representation, its adaptive dynamic processing and fusion.

Suggested Citation

  • K. Dedecius & V. Sečkárová, 2013. "Centralized Bayesian reliability modelling with sensor networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(5), pages 471-482.
    • Handle: RePEc:taf:nmcmxx:v:19:y:2013:i:5:p:471-482
    DOI: 10.1080/13873954.2013.789064
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    References listed on IDEAS

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    1. Xiaogang Wang & James Zidek, 2005. "Derivation of mixture distributions and weighted likelihood function as minimizers of KL-divergence subject to constraints," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(4), pages 687-701, December.
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