Relevance of control theory to design and maintenance problems in time-variant reliability: The case of stochastic viability

Rougé, Charles; Mathias, Jean‐Denis; Deffuant, Guillaume

doi:10.1016/j.ress.2014.07.025

Cited by 15 publications

(6 citation statements)

References 44 publications

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“…Reliability theory was initially developed in the field of mechanical engineering − and was broadly diffused in other domains, especially in environmental management. , In these applications, the system, subject to uncertainties, belongs either to a failure domain or to a safety domain. The purposes of the reliability methods are (1) to approximate the limit decision state surface ( G ( X ) = 0) that separates the failure domain ( G ( X ) < 0) and the reliability domain ( G ( X ) > 0); (2) to deduce (from the approximation of the limit decision state surface) the probability that the system belongs to the failure domain.…”

Section: Methodsmentioning

confidence: 99%

Using the Reliability Theory for Assessing the Decision Confidence Probability for Comparative Life Cycle Assessments

Wei¹,

Larrey-Lassalle²,

Faure³

et al. 2016

Environ. Sci. Technol.

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Comparative decision making process is widely used to identify which option (system, product, service, etc.) has smaller environmental footprints and for providing recommendations that help stakeholders take future decisions. However, the uncertainty problem complicates the comparison and the decision making. Probability-based decision support in LCA is a way to help stakeholders in their decision-making process. It calculates the decision confidence probability which expresses the probability of a option to have a smaller environmental impact than the one of another option. Here we apply the reliability theory to approximate the decision confidence probability. We compare the traditional Monte Carlo method with a reliability method called FORM method. The Monte Carlo method needs high computational time to calculate the decision confidence probability. The FORM method enables us to approximate the decision confidence probability with fewer simulations than the Monte Carlo method by approximating the response surface. Moreover, the FORM method calculates the associated importance factors that correspond to a sensitivity analysis in relation to the probability. The importance factors allow stakeholders to determine which factors influence their decision. Our results clearly show that the reliability method provides additional useful information to stakeholders as well as it reduces the computational time.

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Section: Methodsmentioning

confidence: 99%

Using the Reliability Theory for Assessing the Decision Confidence Probability for Comparative Life Cycle Assessments

Wei¹,

Larrey-Lassalle²,

Faure³

et al. 2016

Environ. Sci. Technol.

Self Cite

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“…They are generally suitable for emergency or real-time applications, but not necessarily for monitoring applications, as environment monitoring, pollution control or agricultural field, where the frequency of collection is specific at different moments and it depends on the evolution stage of a plant for example [9]. For this kind of application (monitoring), a new approach is defined and used in literature: the theory of viability [9], [15]- [18]. It has been applied for the first time in sensors network fields by Kone et al in [19].…”

Section: Related Workmentioning

confidence: 99%

A Viability Approach For Management Of IEEE 802.15.4 Wireless Sensor Node Performance

Sidibe

Kone²,

N'Takpé

et al. 2021

IJEEI

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The long-term use of wireless sensors node while guaranteeing a good Quality of Services (QoS) is a major challenge in wireless sensor networks. Most of the relevant solutions which exist are proposed under Mac layer level but they use an optimization technique which requires a regular update of parameters and leads to unnecessary energy consumptiom which reduces the expected liftime and QoS. So in order to address this issue, we propose in this paper, an adaptive management of wireless sensor node resources to meet application requirements in terms of energy consumption, reliability and delay. To do this, we have used the theory of viability, which is an approach that allows controlling the evolution of a system in a set of desirable states. Here we have proposed an enhanced analytical model of sensor node's energy dynamic, and we control it based on both Mac layer parameters of the IEEE 802.15.4 standard and the packet sampling frequency. The simulation results have shown that the proposed model is more accurate and efficient as a node can send more information without violating energy, reliability and delay constraints.

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“…However, they may come at very different costs and levels of complexity. For example, early warning 10.1029/2018EF000852 approaches (based on variance, for instance) involve efficient measures against uncertainties and avoiding tipping points (Dakos et al, 2008(Dakos et al, , 2012Lenton et al, 2008;Scheffer et al, 2009) while backward techniques enable managers to take into account the dynamics of the system (Brias et al, 2015;Rougé et al, 2013Rougé et al, , 2014Rougé et al, , 2015. Here we highlight the impact of various adaptive management strategies on system collapse.…”

Section: Stylized Adaptive Strategiesmentioning

confidence: 99%

How Does Knowledge Infrastructure Mobilization Influence the Safe Operating Space of Regulated Exploited Ecosystems?

2018

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Decision makers often have to act before critical times to avoid the collapse of ecosystems using knowledge that can be incomplete or biased. Adaptive management may help managers tackle such issues. However, because the knowledge infrastructure required for adaptive management may be mobilized in several ways, we study the quality and the quantity of knowledge provided by this knowledge infrastructure. In order to analyze the influence of mobilized knowledge, we study how the following typology of knowledge and its use may impact the safe operating space of exploited ecosystems:(1)knowledge of the past based on a time series distorted by measurement errors; (2)knowledge of the current systems' dynamics based on the representativeness of the decision makers' mental models of the exploited ecosystem; (3)knowledge of future events based on decision makers' likelihood estimates of extreme events based on modeling infrastructure (models and experts to interpret them) they have at their disposal. We consider different adaptive management strategies of a general regulated exploited ecosystem model and we characterize the robustness of these strategies to biased knowledge. Our results show that even with significant mobilized knowledge and optimal strategies, imperfect knowledge may still shrink the safe operating space of the system leading to the collapse of the system. However, we also show that in some cases imperfect knowledge may unexpectedly increase the safe operating space by suggesting cautious strategies. We leverage the quantitative results to frame a discussion focusing on the importance of understanding subtleties of how adaptive knowledge mobilization and knowledge infrastructure affect the robustness of exploited ecosystems. Key Points:• Knowledge infrastructure influences the quantity (complete or incomplete knowledge) as well as the quality (biased or unbiased) of the knowledge used by managers • To study the impact of knowledge infrastructure on managed ecosystems, we build a typology of knowledge used for regulating uncertain exploited ecosystems • We then assess the robustness of stylized adaptive management strategies to biased knowledge quantitatively and present the results qualitatively in terms of the size of the safe operating space of regulated exploited ecosystems.

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Relevance of control theory to design and maintenance problems in time-variant reliability: The case of stochastic viability

Cited by 15 publications

References 44 publications

Using the Reliability Theory for Assessing the Decision Confidence Probability for Comparative Life Cycle Assessments

Using the Reliability Theory for Assessing the Decision Confidence Probability for Comparative Life Cycle Assessments

A Viability Approach For Management Of IEEE 802.15.4 Wireless Sensor Node Performance

How Does Knowledge Infrastructure Mobilization Influence the Safe Operating Space of Regulated Exploited Ecosystems?

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