基于故障维护树的智能建筑维护高效概率模型检查
Efficient Probabilistic Model Checking of Smart Building Maintenance using Fault Maintenance Trees
AI总结：
本文提出利用概率模型检查构建故障维护树框架，通过抽象算法减少连续时间马尔可夫链规模，提升智能建筑HVAC系统可靠性评估效率。
摘要：
面向智能建筑和电力系统等复杂系统，本文提出故障维护树（FMT）方法，整合故障树与维护策略，通过概率模型检查框架实现系统可靠性和维护策略的高效评估。本文设计了FMT的概率模型检查建模框架，并提出抽象算法以减少等效连续时间马尔可夫链的规模，从而提高模型检查效率。通过评估供暖、通风和空气调节系统FMT的可靠性指标和维护策略，验证了所提方法的有效性。
英文摘要：
Cyber-physical systems, like Smart Buildings and power plants, have to meet high standards, both in terms of reliability and availability. Such metrics are typically evaluated using Fault trees (FTs) and do not consider maintenance strategies which can significantly improve lifespan and reliability. Fault Maintenance trees (FMTs) -- an extension of FTs that also incorporate maintenance and degradation models, are a novel technique that serve as a good planning platform for balancing total costs and dependability of a system. In this work, we apply the FMT formalism to a Smart Building application. We propose a framework for modelling FMTs using probabilistic model checking and present an algorithm for performing abstraction of the FMT in order to reduce the size of its equivalent Continuous Time Markov Chain. This allows us to apply the probabilistic model checking more efficiently. We demonstrate the applicability of our proposed approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system's FMT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
