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Risk Analysis for Critical Infrastructure Systems: An Approach Based onStatistical Learning Theory

机译：关键基础设施系统的风险分析：一种基于统计学习理论的方法

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Probabilistic risk analysis has historically been developed for situations in which measuredrndata is limited or non-existent and expert knowledge is the best source of information available. Thererncontinue to be a number of important problem areas characterized by a lack of hard data. However, inrnother important problem areas the emergence of information technology has transformed the situationrnfrom one characterized by little data to one characterized by data overabundance. Risk assessment forrnlarge-scale, critical infrastructure systems such as electric power distribution systems, transportationrnsystems, water supply systems, and natural gas supply systems are important examples of this class ofrnproblems. There are often substantial amounts of information collected and archived about thernbehavior of these systems over time. Yet it can be difficult to effectively utilize these large data setsrnfor reliability estimation. Using this information for estimating the risk of system failure requires arndifferent approach and analysis paradigm than risk analysis for data-poor systems does. Statisticalrnlearning theory, a diverse set of methods designed to draw inferences from large, complex data sets,rncan provide a basis for risk analysis for data-rich systems.
机译：历史上已经针对概率数据有限或不存在并且专家知识是可用信息的最佳来源的情况开发了概率风险分析。继续存在许多以缺乏硬数据为特征的重要问题领域。但是，在另一个重要的问题领域，信息技术的出现将这种情况从一种以少量数据为特征的情况转变为以大量数据为特征的情况。大型关键基础设施系统（如配电系统，运输系统，供水系统和天然气供应系统）的风险评估是此类问题的重要示例。随着时间的推移，通常会收集和存档有关这些系统行为的大量信息。然而，可能难以有效地利用这些大数据集来进行可靠性估计。与数据贫乏的系统进行风险分析相比，使用此信息来估计系统故障的风险需要采用不同的方法和分析范式。统计学习理论是设计用于从大型，复杂的数据集中进行推论的多种方法，可以为数据丰富的系统的风险分析提供基础。

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