Resilience analysis: A formulation to model risk factors on complex system resilience

Mottahedi, Adel; Sereshki, Farhang; Ataei, Mohammad; Qarahasanlou, Ali Nouri; Barabadi, Abbas

doi:10.1007/s13198-021-01131-w

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…In later years, authors such as Xu and Li, Misra et al, and Giorgio et al discussed the properties of the frailty model [35][36][37]. Moreover, recently this model was used in spar part estimation, remaining useful life (RUL), recoverability, failure data analysis, and resilience analysis [21][22][23][24]38,39]. According to this model, the reliability of each component (R(t; z; z(t)|α)) can be modelled as follows [21][22][23][24]:…”

Section: Methodology and Framework: Risk Factor-based Reliability Importance Measure (Rf-rim)mentioning

confidence: 99%

“…where R θi (t; z; z(t)) is the item's reliability function and considering the existence of observable and unobservable risk factors. If there is no effect from unobservable risk factors, then α = 1, and Equation ( 6) will reduce to the Cox regression model as follows [22,31,39]:…”

Section: Methodology and Framework: Risk Factor-based Reliability Importance Measure (Rf-rim)mentioning

confidence: 99%

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Resilience Assessment: A Performance-Based Importance Measure

et al. 2021

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The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system’s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study.

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Section: Methodology and Framework: Risk Factor-based Reliability Importance Measure (Rf-rim)mentioning

confidence: 99%

Section: Methodology and Framework: Risk Factor-based Reliability Importance Measure (Rf-rim)mentioning

confidence: 99%

Resilience Assessment: A Performance-Based Importance Measure

et al. 2021

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“…Rukovaoci dampera rade u otežanim uslovima rada [11,12]. Dodatno, podaci o smrtnim slučajevima u vezi sa damperima pokazuju visoke brojeve i preporučuju se brojne mere za unapređenje bezbednosti [13][14][15]. Potrošnja goriva dampera čini oko 30% ukupne potrošnje energije u površinskim kopovima [16][17].…”

Section: Rezultati Snimanja Rada Damperaunclassified

Dumper operation risk analysis model and HAMRISK expert system

Misita¹,

Spasojević-Brkić²,

Rakonjac³

et al. 2022

Tehnika

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The paper presents a model of risk analysis of dumper operation with the aim of determining the most common reason for unplanned stoppages, as well as, after classifying stoppages by system, to evaluate them from the aspect of risk. The set criteria for risk analysis are: time spent in downtime, frequency of downtime and level of danger. The results of the research indicated the causes of high risk on the observed machines, on the basis of which priority preventive maintenance measures can be refined in order to reduce risk. Furthermore, in the research on risk prevention of the operation of transport and mining machines, the online contextual and adaptive expert system HAMRISK was developed and the advantages and disadvantages of the developed software solution were examined in practice.

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“…However, these few developed mechanisms are limited when the outage is hard or impossible to detect. Furthermore, network failures face a lack of management and of anticipation strategy [5]. Besides, most prior works in RAN resilience enhancement propose resilient mechanisms without quantifying the improvements observed with resilience metrics [5].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, network failures face a lack of management and of anticipation strategy [5]. Besides, most prior works in RAN resilience enhancement propose resilient mechanisms without quantifying the improvements observed with resilience metrics [5]. This makes the majority of resilience solutions in the RAN domain purely qualitative.…”

Section: Introductionmentioning

confidence: 99%

Resilience analysis and quantification method for 5G-Radio Access Networks

Kaada

Morel

Rubino

et al. 2022

2022 13th International Conference on Network of the Future (NoF)

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A 5G Radio Access Network (5G-RAN) can be disturbed or shutdown due to a variety of failures, in spite of advanced optimization techniques and self-healing methods. Recently, operators started to take an interest in improving the resilience of communication networks with adaptive compensation techniques to mitigate outageinduced performance degradation. However, for the sake of effective and efficient resilience management, it is vital to be able to measure current and prospective resiliency levels of a given 5G-RAN using relevant and explicit metrics. Thus, the characterisation of resilience goes over a thorough analysis of 5G-RAN performance indicators followed by a rigorous quantification of current and future levels of resilience. In this work, we perform an analysis and a quantification of 5G-RAN resilience using a coverage indicator. It is known as a main performance indicator for network planners and operators, coverage is a necessary prerequisite to ensure a certain level of Quality of Service.For that, we model the network coverage using Continuous Time Markov Chains (CTMCs) where coverage status is characterized with multiple states defined with Reference Signal Received Power (RSRP) signal. The proposed Markov model is analytically studied allowing to perform quantitative analysis, predict coverage outage and provide resilience quantification. Using our model, we conduct numerical analysis of several usage scenarios and propose a resilience framework to show the usability of our proposed approach.

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Resilience analysis: A formulation to model risk factors on complex system resilience

Cited by 9 publications

References 31 publications

Resilience Assessment: A Performance-Based Importance Measure

Resilience Assessment: A Performance-Based Importance Measure

Dumper operation risk analysis model and HAMRISK expert system

Resilience analysis and quantification method for 5G-Radio Access Networks

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