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首页> 外文期刊>Artificial intelligence for engineering design, analysis and manufacturing >Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems
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Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

机译:安全性强的设计:使用子图分析来引发复杂系统中的危险紧急失效行为

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摘要

Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.
机译:在早期的设计过程中,要确定由组件故障和相互作用引起的故障路径和潜在的危险情况是一个挑战。大型工程系统中存在的固有复杂性导致非显而易见的紧急情况,可能导致不可预见的危害。当前的危害分析技术着重于单一危害(故障树),单一故障(事件树)或域中已知危害的列表(危害识别)。在设计复杂系统的早期,工程师可能会将其系统表示为功能模型。然后,功能故障推理工具可以详尽地模拟定性故障场景。可以通过工程师指定的危险规则将某些情况识别为危险,但是目标是识别代表未知危险的情况。已知危险场景的图形表示中特定子图的发生率用于训练分类器,以区分危险与非危险。该算法识别最有可能危险的情况,并将其呈现给工程师。在查看了场景并判断了其安全性之后,工程师可能具有洞察力来制定其他危害规则。通过计算机和人为判断对战略场景进行战略展示的协作过程将确定以前未知的危害。已经在相对简单的电力系统功能模型上测试了该方法的可行性,并取得了积极的成果。将功能故障推理应用于机器人漫游车团队的相关工作将提供来自更复杂系统的数据。

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