Optimal sensor positioning on pressurized equipment based on Value of Information

Hoseyni, Seyed Mojtaba; Maio, Francesco Di; Zio, Enrico

doi:10.1177/1748006x21989661

Cited by 4 publications

(4 citation statements)

References 21 publications

(34 reference statements)

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“…For example, Mean Absolute Error (MAE) or Root Mean Square Error (RMSE) can be used, although these metrics may be too much dependent on the quality of the available data; Bayesian measures, like the Bayes factor (Berger and Pericchi, 1996), account for both data and model belief, making them more appealing for GBM quality assessment (Rai and Sahu, 2020). Value of Information (VoI), a Bayesian-based method to evaluate the improvements of a priori knowledge via new information (Hoseyni et al, 2021), may also result useful for discerning whether the reliance on additional BBMs or WBMs may be beneficial for GBM developments.…”

Section: Grey-box Models: Alternativesmentioning

confidence: 99%

Grey-Box Models for Cyber-Physical Systems Reliability, Safety and Resilience Assessment

Futalef,

Maio,

Zio

2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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Cyber-Physical Systems (CPSs) integrate physical components with cybernetic elements. Modeling the complex interactions that arise among these is necessary to realistically represent the physical processes, the interconnections among physical components and cybernetic elements, and the data transmission within the cyber network. The computational effort needed for the solution of such a model challenges the CPSs reliability, safety and resilience assessment, which implies simulating a large number of scenarios. Grey-Box Models (GMB), which combine physics-based and data-driven models, offer a way to tackle the problem, while keeping model accuracy and preserving interpretability. In this work, we elaborate on a hierarchy-based architecture of literature to develop a systematic methodology in support of the development of GBMs for CPSs. The methodology is exemplified by developing the GBM of an Integrated Power-Telecommunication (IP&TLC) CPS infrastructure.

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Section: Grey-box Models: Alternativesmentioning

confidence: 99%

Grey-Box Models for Cyber-Physical Systems Reliability, Safety and Resilience Assessment

Futalef,

Maio,

Zio

2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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“…The VoI represents the expected reduction in management costs associated with the acquisition of new information [6][7][8]. This concept has been applied in various areas such as emergency management [9,10], sensor deployment optimization [11,12], and maintenance strategy definition [13,14]. Generally, VoI analyses are carried out under the assumption that the SHM system is always working correctly.…”

Section: Introductionmentioning

confidence: 99%

Value of information analysis accounting for data quality

Giordano

Quqa

Limongelli

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Structural Health Monitoring (SHM) can provide valuable information for maintenance-related activities and post-disaster emergency management. However, as with any technological system, SHM systems can be susceptible to errors due to malfunctioning. Therefore, it is essential to assess the potential for malfunctions and the associated costs of maintenance and repair when evaluating the long-term benefits of SHM systems. In the last two decades, sensor validation tools (SVTs) have been proposed to support decisions by isolating and discarding abnormal data. Recently, the authors of this paper have proposed a framework based on the Value of Information (VoI) from Bayesian decision analysis to account for different states of an SHM system and assess the benefit of SVT information. By quantifying the additional value obtained from SVTs, decision-makers can make more informed decisions about investing in these systems. This framework is here demonstrated on a real case study, namely the S101 bridge in Austria, which has been artificially damaged for research purposes. The benefit of collecting SHM and SVT information is quantified by considering a simple decision problem related to the management of the bridge in the aftermath of a damaging event. Overall, the study highlights the potential benefits of using SVTs to improve the reliability of SHM data and inform decision-making in the management of structures.

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“…To overcome this issue, combinatorial optimization methods can be used to find the optimal positions not by an intractable exhaustive search but, rather, by a smart iterative selection of a subset of possible solutions that, eventually, can lead to the optimal solution. 14,15 The optimization methods that have, so far, been used for solving VoI-based sensors positioning problems 16 are here categorized in greedy and non-greedy.…”

Section: Introductionmentioning

confidence: 99%

Subset simulation for optimal sensors positioning based on value of information

Hoseyni

Maio

Zio

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Greedy and non-greedy optimization methods have been proposed for maximizing the Value of Information (VoI) for equipment health monitoring by optimal sensors positioning. These methods provide good solutions, but still with limitations and challenges: greedy optimization does not guarantee to find the optimal solution, due to the non-submodularity of the VoI; non-greedy optimization does not suffer from the non-submodularity of the VoI but requires computationally expensive and tedious simulations to find the optimal solution. In this work, the Subset Simulation (SS) method is originally proposed to address these limitations and challenges. A real case study is considered concerning the condition monitoring of a Steam Generator (SG) of a Prototype Fast Breeder Reactor (PFBR). Results show that SS, even if initialized with a small number of Monte Carlo samples, is capable of finding the optimal set of sensors positions in a very short computational time and is insensitive to the non-submodularity of VoI.

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Optimal sensor positioning on pressurized equipment based on Value of Information

Cited by 4 publications

References 21 publications

Grey-Box Models for Cyber-Physical Systems Reliability, Safety and Resilience Assessment

Grey-Box Models for Cyber-Physical Systems Reliability, Safety and Resilience Assessment

Value of information analysis accounting for data quality

Subset simulation for optimal sensors positioning based on value of information

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