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A classification-based approach to monitoring the safety of dynamic systems

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  • Zhong, Shengtong
  • Langseth, Helge
  • Nielsen, Thomas Dyhre

Abstract

Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is stable. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this problem setting, we propose a general model for classification in dynamic domains, and exemplify its use by showing how it can be employed for activity detection. We construct our model by using well known statistical techniques as building-blocks, and evaluate each step in the model-building process empirically. Exact inference in the proposed model is intractable, so in this paper we experiment with an approximate inference scheme.

Suggested Citation

  • Zhong, Shengtong & Langseth, Helge & Nielsen, Thomas Dyhre, 2014. "A classification-based approach to monitoring the safety of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 61-71.
    • Handle: RePEc:eee:reensy:v:121:y:2014:i:c:p:61-71
    DOI: 10.1016/j.ress.2013.07.016
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    References listed on IDEAS

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    1. Kohda, Takehisa & Cui, Weimin, 2007. "Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1716-1723.
    2. Jan Erik Vinnem, 2007. "Offshore Risk Assessment," Springer Series in Reliability Engineering, Springer, edition 2, number 978-1-84628-717-6, February.
    3. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    4. Zamalieva, Daniya & Yilmaz, Alper & Aldemir, Tunc, 2013. "A probabilistic model for online scenario labeling in dynamic event tree generation," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 18-26.
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