Reliability and importance measure analysis of networks with shared risk link groups

Vaisman, Radislav; Sun, Yuting

doi:10.1016/j.ress.2021.107578

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…2,3 It has been widely applied in various fields. 4,5 The traditional importance measure analysis considers the influence of components on the system reliability based on the binary-state assumption of the system. Based on the hypothesis of binary-state, Lyu and Si 6 proposed the importance measure of the system under repeated random loads and carried out a reliability analysis of the system by using the order statistics theory.…”

Section: Introductionmentioning

confidence: 99%

Integrated availability importance measure for multi-state complex systems analysis

Yuhong²,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the system.

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Section: Introductionmentioning

confidence: 99%

Integrated availability importance measure for multi-state complex systems analysis

Yuhong²,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…For instance, product structures are not significantly affected by the failure of noncritical modules, but they will rapidly fragment when key modules (hubs) fail 8 . The vulnerability of product structures under FKC will reduce the stability and safety of products and increase the cost of maintenance and redesign 9 , 10 . Therefore, to keep the product structure stable and control cost in the product lifetime, it is necessary to improve product structural robustness under FKC .…”

Section: Introductionmentioning

confidence: 99%

Improve the product structural robustness based on network motifs in product development

et al. 2022

Sci Rep

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The stability and safety of products will be reduced if product structures are vulnerable to failures of key components. Existing methods for improving product structural robustness mainly focus on some key components, but they cannot provide designers with universal and explicit structure optimization strategies. From the viewpoint of product structural networks, the motif is the fundamental meta-structure, and it is efficient to analyse product structural properties. Motivated by this, strategies to improve product structural robustness are explored by considering relationships between typical motifs and product structural robustness. First, product structural networks are constructed by collecting the structural information of a series of product generations. Second, typical (anti-) motifs are identified based on an enumeration algorithm, and the robustness is measured considering the largest connected cluster. Then, relationships between the frequency of different motifs and product structural robustness are obtained through principal component regression. The results of a case study on the smartphone show that anti-motifs are negative for product structural robustness. Motifs with loop structures are positive for product structural robustness. Accordingly, relevant strategies to improve product structural robustness in product development are developed.

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Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models

Petrenko,

Grigorieva,

Petrenko

et al. 2024

Lecture Notes in Networks and Systems

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Reliability and importance measure analysis of networks with shared risk link groups

Cited by 9 publications

References 34 publications

Integrated availability importance measure for multi-state complex systems analysis

Integrated availability importance measure for multi-state complex systems analysis

Improve the product structural robustness based on network motifs in product development

Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models

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