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

复杂的工程系统，如能源生产、航空和机器人行业中的系统，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部件，并严格测试潜在的设计替代方案。在这一过程中，安全特性通常是在制造之前验证的，而不是从最早的阶段起就在指导设计方面发挥有意义的作用。这种时间和成本密集的测试和失败后重新设计的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套指标，允许设计人员在设计周期的早期识别和比较不同系统替代方案的安全性。系统工程师传统上只使用启发式可靠性度量来解决安全问题，或者在设计阶段接近尾声时，使用蒙特卡洛分析或概率风险分析。最近在设计决策和形式化系统建模方面的研究为在设计阶段推进系统分析和测试提供了支持。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。具体地说，这项研究的目的是通过对数千种复杂的故障场景应用统计聚类方法来分类和理解系统故障，从而加深对导致安全运行的系统行为的理解。通过使用危险本体论和自动生成潜在故障路径的搜索算法，将能够探索潜在的故障行为。将确定指标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属性评估和比较设计架构和技术替代方案。来自这项研究的方法和基于安全的评估将使用由机器人漫游车合作团队组成的新型复杂系统设计试验台进行验证。

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.