What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance: What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance
What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance: What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance
复制标题
是什么让长期监测变得方便？
DOI：
10.1002/stc.2329
复制
发表时间：
2019
影响因子：
5.4
通讯作者：
Pozzi, Matteo
中科院分区：
文献类型：
--
作者：
Li, Shuo;Pozzi, Matteo
监控系统收集的信息只有在特定条件下才能为基础设施部件的运行和维护提供显著的经济效益。信息必须是精确的，不冗余的，与不确定情况下的相关决策问题相关，例如，维护操作的适当调度，并且决策者需要能够处理该信息并及时做出反应。所有这些考虑都可以自然地嵌入到信息价值（VoI）中，这是一种基于效用的指标，用于评估不确定情况下决策中附加信息的影响。在本文中，我们研究了VoI与监测系统、部件劣化和决策过程的关键特征之间的关系，包括测量精度和可用性、劣化率、损坏可预测性、反应时间、维护成本和经济折扣因子。通过利用以前的工作，我们将维护过程建模为部分可观察的马尔可夫决策过程，并计算长期监测的VoI。我们建议的框架允许对这些特征的共同影响进行详细的定量分析，并可用于确定监测效益高的条件，在值得监测的组成部分之间分配优先次序，或优化用于监测工作的资源分配。
Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. The information has to be precise, not redundant, related to relevant decision problems under uncertainty as, for example, the appropriate scheduling of maintenance actions, and the decision maker needs to be able to process that information and react timely. All these considerations can be naturally embedded in the value of information (VoI), a utility‐based metric for assessing the impact of the additional information in decision making under uncertainty. In this paper, we investigate the relation between the VoI and key features of the monitoring system, of the component deterioration and of the decision‐making process, including measure accuracy and availability, deterioration rates, damage predictability, reaction time, maintenance costs, and the economic discount factor. By leveraging previous work, we model the maintenance process as a partially observable Markov decision process, and we compute the VoI of long‐term monitoring. Our proposed framework allows for a detailed quantitative analysis on the joint effects of these features and can be useful to identify conditions when the benefit of monitoring is high, to assign priorities among components that deserve to be instrumented or to optimize the allocation of resources to monitoring efforts.