ArticlePloS one2022
System resilience distribution identification and analysis based on performance processes after disruptions.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Resilience is a system's ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system's performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.
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