Collaborative Research: Improving the Safety of Complex Engineered Systems by Identifying Failure Paths Early in the Design Process

协作研究：通过在设计过程的早期识别故障路径来提高复杂工程系统的安全性

• 批准号: 1363349
• 负责人: David Jensen
• 金额: $ 14.28万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1363349&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Safety; Complex

Complex engineered systems, such as those found in energy production, aviation and robotic industries rely on the integration of multiple technical, scientific and engineering disciplines. The traditional practice to ensure the safe operation of these systems is to use highly reliable components and to rigorously test potential design alternatives. Under this process, safety characteristics are often validated just prior to manufacturing instead of having a meaningful role in directing design from the earliest stages. This time and cost intensive approach of testing and redesigning after failures limits the competitiveness of the United States' advanced manufacturing capabilities. This award supports fundamental research for developing a new paradigm for safety-driven early-phase design, and for validating the corresponding system design theories. The immediate outcome of this research is a set of metrics that can allow designers to identify and compare the safety of different system alternatives early in the design cycle. Systems engineers have traditionally addressed safety only using heuristic reliability metrics or, towards the end of the design phase, using Monte Carlo analysis or Probabilistic Risk Analysis. Recent research in design decision making and formal systems modeling supports moving system analysis and testing forward in the design phase. Building on these recent trends, this award focuses on considering safety in early design. Specifically, this research aims to develop an understanding of system behaviors that lead to safe operation, by applying statistical clustering methods to thousands of complex failure scenarios to classify and understand system faults. The exploration of potential failure behaviors will be enabled through the use of hazard ontologies and search algorithms that automatically generate potential failure paths. Metrics will be identified to help system engineers identify the risk of hazardous system behaviors. Further, these metrics will enable designers to evaluate and compare design architectures and technological alternatives in the early design stage based on safety properties. The methods and safety-based assessments that are derived from this research will be validated using a novel complex system design testbed, consisting of a cooperative team of robotic rovers.

复杂的工程系统，如能源生产、航空和机器人工业中的系统，依赖于多个技术、科学和工程学科的整合。确保这些系统安全运行的传统做法是使用高度可靠的组件，并严格测试潜在的设计方案。在此过程中，安全特性通常在制造之前进行验证，而不是在最早阶段指导设计中发挥有意义的作用。这种在失败后进行测试和重新设计的时间和成本密集型方法限制了美国先进制造能力的竞争力。该奖项支持基础研究，为安全驱动的早期设计开发新的范例，并验证相应的系统设计理论。这项研究的直接结果是一组指标，可以让设计人员在设计周期的早期识别和比较不同系统的安全性。传统上，系统工程师只使用启发式可靠性指标来解决安全问题，或者在设计阶段结束时使用蒙特卡洛分析或概率风险分析。最近的研究在设计决策和正式的系统建模支持移动系统分析和测试在设计阶段前进。在这些最新趋势的基础上，该奖项侧重于在早期设计中考虑安全性。 具体而言，本研究旨在通过将统计聚类方法应用于数千个复杂的故障场景来分类和理解系统故障，从而了解导致安全操作的系统行为。潜在故障行为的探索将通过使用自动生成潜在故障路径的危险本体和搜索算法来实现。将确定系统故障，以帮助系统工程师识别危险系统行为的风险。此外，这些指标将使设计人员能够在早期设计阶段根据安全性能评估和比较设计架构和技术方案。从这项研究中得出的方法和基于安全的评估将使用一个新的复杂系统设计试验台进行验证，该试验台由一个机器人漫游车合作团队组成。