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Value-of-information in spatio-temporal systems: Sensor placement and scheduling

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  • Malings, C.
  • Pozzi, M.

Abstract

The management of infrastructure involves accounting for factors which vary in space over the system domain and in time as the system changes. Effective system management should be guided by models which account for uncertainty in these influencing factors as well as for information gathered to reduce this uncertainty. In this paper, we address the problem of optimal information collection for spatially distributed dynamic infrastructure systems. Based on prior information, a monitoring scheme can be designed, including placement and scheduling of sensors. This scheme can be adapted during the management process, as more information becomes available. Optimality can be defined in terms of the value of information (VoI), which provides a rational metric for quantifying the benefits of data gathering efforts to support system management decision-making. However, the computation of this metric in spatially and temporally extensive systems can present a practical impediment to its implementation. We describe this complexity, and investigate a special case of system topology, termed as a temporally decomposable system with uncontrolled evolution, in which the complexity of assessing VoI grows at a manageable rate with respect to the system management time duration. We demonstrate the evaluation and optimization of the VoI in an example of such a system.

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  • Malings, C. & Pozzi, M., 2018. "Value-of-information in spatio-temporal systems: Sensor placement and scheduling," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 45-57.
    • Handle: RePEc:eee:reensy:v:172:y:2018:i:c:p:45-57
    DOI: 10.1016/j.ress.2017.11.019
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    Cited by:

    1. Kapoor, Medha & Christensen, Christian Overgaard & Schmidt, Jacob Wittrup & Sørensen, John Dalsgaard & Thöns, Sebastian, 2023. "Decision analytic approach for the reclassification of concrete bridges by using elastic limit information from proof loading," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Song, Chaolin & Zhang, Chi & Shafieezadeh, Abdollah & Xiao, Rucheng, 2022. "Value of information analysis in non-stationary stochastic decision environments: A reliability-assisted POMDP approach," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Zou, Guang & Kolios, Athanasios, 2022. "Quantifying the value of negative inspection outcomes in fatigue maintenance planning: Cost reduction, risk mitigation and reliability growth," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Malings, C. & Pozzi, M., 2019. "Submodularity issues in value-of-information-based sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 93-103.
    5. Lin, Chaochao & Song, Junho & Pozzi, Matteo, 2022. "Optimal inspection of binary systems via Value of Information analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    7. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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