How to learn from the resilience of Human–Machine Systems?

Ouedraogo, Kiswendsida Abel; Enjalbert, Simon; Vanderhaegen, Frédéric

doi:10.1016/j.engappai.2012.03.007

Cited by 50 publications

(7 citation statements)

References 33 publications

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“…Rate-based metrics quantify how system performance changes over time. The resilience curve's derivative, broadly, has been labeled agility [227], [228] or local resilience [229], [230]. Most commonly, rate-based metrics focus on the failure or recovery phases.…”

Section: Rate-based Metricsmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

111

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Section: Rate-based Metricsmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

111

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“…While the responding and monitoring within socio-technical systems is often performed by the technical components, the human being is able to take over tasks which serve the learning and anticipation ability of the entire system. Human operators are able to gain experience and to learn by repeating their control tasks, thus improving their behaviour [134]. They can adjust themselves according to the dynamic changes of the socio-technical systems.This requires adaptive and proactive behaviour (i.e.…”

Section: Application Of Resilience Metrics In a Socio-technical Systemmentioning

confidence: 99%

Strategies for Mastering Uncertainty

Pfetsch

Abele

Altherr

et al. 2021

Springer Tracts in Mechanical Engineering

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This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.

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“…6, JUNE 2020 Transactions of the ASME resilience metrics in the context of deterministic models were, e.g., suggested by Refs. [25][26][27][28][29]. Pathwise metrics do not rely on probabilities and do not capture quantities that depend on probabilities-such as the rates of occurrences of disruptive events and the distributions or moments of the random size of disruptions or the random times of their recovery.…”

Section: Theoretical Fundamentalsmentioning

confidence: 99%

Resilience Decision-Making for Complex Systems

Salomon

Broggi

Kruse

et al. 2020

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

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Complex systems—such as gas turbines, industrial plants, and infrastructure networks—are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.

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How to learn from the resilience of Human–Machine Systems?

Cited by 50 publications

References 33 publications

Infrastructure resilience curves: Performance measures and summary metrics

Infrastructure resilience curves: Performance measures and summary metrics

Strategies for Mastering Uncertainty

Resilience Decision-Making for Complex Systems

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