Reliability Assessment of a Multi-Redundant Repairable Mechatronic System

Martín, Carmen; González-Prida, Vicente; Pérès, François

doi:10.1007/978-3-319-07167-1_14

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…By doing so, the paper aims to contribute to the sustainability and effectiveness of shale gas exploitation in this region. Through rigorous analysis and innovative techniques, this research endeavors to pave the way for more efficient and responsible practices in the realm of hydraulic fracturing [4,[10][11][12][13].…”

Section: Case Studymentioning

confidence: 99%

“…Utilizing hierarchical graphs, the BBN adeptly captures probabilistic causal relationships among uncertain variables, which are crucial when confronted with limited data. This dynamic model learns continuously from both historical and real-time data, adapting its understanding during hydraulic fracturing processes based on observed changes [6,12,21]. The categorization of nodes into root nodes (RN), consequence nodes (CN), and safety nodes (SN) enhances the system's fault identification capabilities.…”

Section: Building a Bbn Modelmentioning

confidence: 99%

“…This adaptive model, tailored for hydraulic fracturing intricacies, ensures continual learning and updates, contributing to effective fault identification and, consequently, heightened system safety and performance. In essence, the BBN model provides a comprehensive framework that amalgamates historical and real-time data, categorizes nodes meaningfully, and utilizes conditional probabilities to evaluate fault scenarios, ultimately enhancing system resilience in hydraulic fracturing [12,14,22].…”

Section: Building a Bbn Modelmentioning

confidence: 99%

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A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

Badjadi,

Zhu,

Zhang

et al. 2023

Water

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The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

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Section: Case Studymentioning

confidence: 99%

Section: Building a Bbn Modelmentioning

confidence: 99%

Section: Building a Bbn Modelmentioning

confidence: 99%

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A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

Badjadi,

Zhu,

Zhang

et al. 2023

Water

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“…The work [30] deals with the reliability modeling and assessing of a mechatronic subsystem used for an UAV flight control with a triple modular redundancy. Markov chains and Reliability Block Diagrams are used for graphic representation of the proposed model.…”

Section: Drone Fleet Survivability and Reliability Assessmentmentioning

confidence: 99%

Drone Fleet Survivability Evaluation Based on Lanchester’s Modified Deterministic Model

Fesenko

Kharchenko

Bardis

et al. 2020

International Journal of Circuits, Systems and Signal Processing

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An algorithmic approach for the assessment of the survivability is proposed that is based on Lanchester’s modified deterministic model. Methods are suggested for increasing the available time capability for nuclear power plant monitoring and coverage, using the required or a limited number of the operable drones,. Dependencies of the variance between the residual fleet damage and permissible drone fleet damage on monitoring time as well as dependencies of the monitoring time on the recovery group productivity are analysed.

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基于mpps 模型的机电系统故障传播路径搜索方法

Wang

Shi

2018

J. Cent. South Univ.

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Reliability Assessment of a Multi-Redundant Repairable Mechatronic System

Cited by 3 publications

References 18 publications

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

Drone Fleet Survivability Evaluation Based on Lanchester’s Modified Deterministic Model

基于mpps 模型的机电系统故障传播路径搜索方法

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