Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach

Compare, Michele; Maio, Francesco Di; Zio, Enrico; Carlevaro, Fausto; Mattafirri, Sara

doi:10.1177/1748006x16651988

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…The number of states can often be derived from descriptive maintenance data. For instance, in industrial applications such as gas turbines [53,54], maintenance teams can assign a qualitative tag to specify the observed health condition of the equipment at the time of inspection. In other cases, the evolution of degradation processes can be sub-divided into successive phases which exhibit physically different degradation mechanisms [55] and which can therefore be described by corresponding multi-state model.…”

Section: Case Studymentioning

confidence: 99%

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Mancuso

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Salo

et al. 2021

Reliability Engineering & System Safety

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Section: Case Studymentioning

confidence: 99%

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Mancuso

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Salo

et al. 2021

Reliability Engineering & System Safety

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“…The survival signature entries that have not been estimated by MCS, are the missing data to be retrieved. For this, an ensemble of Artificial Neural Networks (ANNs) [21] are trained on the available, incomplete data of the sparse survival signature [22], which forms the training dataset. The ANN has been selected to approximate the survival signature because it is a well-known, widely spread tool in ML method.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

Di Maio,

Pettorossi,

Zio

2023

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Survival signature can be useful for the reliability assessment of critical infrastructures. However, analytical calculation and Monte Carlo Simulation (MCS) are not feasible for approximating the survival signature of large infrastructures, because of the complexity and computational demand due to the large number of components. In this case, efficient and accurate approximations are sought. In this paper we formulate the survival signature approximation problem as a missing data problem. An ensemble of Artificial Neural Networks (ANNs) are trained on a set of survival signatures obtained by MCS. The ensemble of trained ANNs is, then, used to retrieve the missing values of the survival signature. A numerical example is worked out and recommendations are given to design the ensemble of ANNs for large-scale, real-world infrastructures. The electricity grid of Great Britain, the New England power grid (IEEE 39-Bus Case), the reduced Berlin metro system and the approximated American Power System (IEEE 118-Bus Case) are, then, eventually, analyzed as particular case studies.

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Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach

Abstract: This paper describes a Monte Carlo (MC) based approach for reconstructing missing information in a dataset

Cited by 2 publications

References 23 publications

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

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