Value of information for spatially distributed systems: Application to sensor placement. (October 2016)

• Record Type: Journal Article
• Authors: Malings, Carl; Pozzi, Matteo
• Is Part Of: Reliability engineering & system safety. Volume 154(2016:Oct.)
• Page Start: 219
• Page End: 233
• Publication Date: 2016-10
• Subjects: Pre-posterior analysis -- Sensor placement -- Gaussian random fields; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.05.010
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: This paper investigates how the value of information (VoI) metric can guide information collection and optimal sensor placement in spatially distributed systems. VoI incorporates relevant features to decision-making, such as uncertainty about the state of the system, precision of measurements, the availability of intervention actions, and the overall cost of managing the system. Spatially distributed systems also allow for information propagation, i.e. measurements collected at one location can be used to update knowledge at other related locations. In this paper, while restricting our attention to Gaussian random field and binary state models, we illustrate first how sensor placements depend on the decision-making problem to be addressed, as encoded in a problem-specific loss function, and second how the complexity of VoI computations is impacted by this loss function's characteristics. In doing so, we consider several loss functions and present computational techniques for evaluating VoI under them. Finally, we apply these techniques to efficiently optimize sensor placements by the VoI metric in two example applications. Highlights: On the Value of Information in systems modeled by Gaussian random fields. Investigation of alternative loss functions and available maintenance actions. Discussion on the computational complexity depending on the loss function.