通过运行状况监视和重新配置实现系统弹性

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2023-11-03 DOI:10.1145/3631612
Ion Matei, Wiktor Piotrowski, Alexandre Perez, Johan de Kleer, Jorge Tierno, Wendy Mungovan, Vance Turnewitsch
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引用次数: 0

摘要

我们展示了一个端到端框架,以提高人工系统对不可预见事件的弹性。该框架基于基于物理的数字孪生模型和三个模块,负责实时故障诊断、预测和重新配置。故障诊断模块使用基于模型的诊断算法检测和隔离故障,并在系统中产生干预措施,消除不确定诊断方案的歧义。通过使用基于物理的数字孪生的并行化和代理模型,我们将故障诊断算法扩展到所需的实时性能。预测模块跟踪故障进展并训练在线退化模型来计算系统组件的剩余使用寿命。此外,我们使用退化模型来评估故障进展对操作需求的影响。重构模块采用基于pddl的规划,赋予语义附件,对系统控制进行调整,使故障对系统运行的影响最小化。我们定义了一个弹性度量,并使用一个燃料系统示例来演示该度量是如何随着我们的框架而改进的。
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System Resilience through Health Monitoring and Reconfiguration
We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based digital twin. The prognostics module tracks fault progression and trains the online degradation models to compute remaining useful life of system components. In addition, we use the degradation models to assess the impact of the fault progression on the operational requirements. The reconfiguration module uses PDDL-based planning endowed with semantic attachments to adjust the system controls to minimize the fault impact on the system operation. We define a resilience metric and use a fuel system example to demonstrate how the metric improves with our framework.
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来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
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