ERI: Design of Reliable Autonomous Engineering Systems with Active Failure Prevention

这个工程研究启动（ERI）项目的目标是创建一个设计框架，使用计算机模拟来设计可靠的自主工程系统（AES），可以主动预防操作过程中的灾难性故障。自主工程系统，如自动驾驶汽车和工业机器人，是新兴的工程系统，正在社会的许多方面取代人类。可以设想，未来的AES应该具有不仅自主地完成使命，而且主动地防止操作中的故障（即，主动故障预防）。通过不确定性条件下的仿真设计，将设计后的主动失效预防融入到AES的早期设计阶段，从而降低所设计的AES投入运行时的失效概率。该研究将建立一个理论基础，使：（1）有效和准确的分析AES配备主动故障预防存在的不确定性的可靠性;和（2）AES的物理和网络系统的协同设计，主动故障预防受到特定的可靠性要求。该研究将使AES的设计具有可靠性保证。它将通过减少设计过程中的可靠性认证工作来加速AES的发展。该项目的其他成果包括可靠性和设计相关课程的改进，本科生和研究生的研究经验，该项目的总体目标是创造一种新的设计范式，从传统的基于可靠性的工程系统设计，通过被动可靠性保证，基于可靠AES的设计，以兼顾群体层面的被动可靠性保证和个体层面的主动故障预防。个性化的主动故障预防和自主设置AES除了传统的工程系统，使目前的设计不确定性的方法不适用于AES。该研究利用基于模型的设计，统计，机器学习和优化，以填补现有方法中的差距。它将通过首次通过方法和协同模拟解决计算挑战，同时考虑主动故障预防，沿着通过解耦和分散基于可靠性的协同设计问题来进行设计优化的创新，从而在可靠性分析方面创造创新。该研究具有广泛的社会影响，使AES的设计具有主动故障预防功能，并考虑到可靠性，这加速了下一代可靠的自主工程系统的开发，以取代人类在安全关键或危险的使命/工作环境中的工作。综合教育计划包括课程改进、让本科生参与研究、传播研究成果以及向K-12学生和教师推广。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this Engineering Research Initiation (ERI) project is to create a design framework using computer simulations to design reliable autonomous engineering systems (AES) that can proactively prevent catastrophic failures during operation. Autonomous engineering systems, such as autonomous vehicles and industrial robots, are emerging engineering systems that are replacing humans in many aspects of society. It is envisioned that AES in the future should have the capability of not only autonomously accomplishing a mission, but also proactively preventing failures in operation (i.e., active failure prevention). Through simulation-based design under uncertainty, this research will incorporate post-design active failure prevention into the early design stage of AES, thus reducing the probability of failure when the designed AES is put into operation. The research will establish a theoretical foundation that enables: (1) efficient and accurate analysis of the reliability of AES equipped with active failure prevention in the presence of uncertainty; and (2) co-design of AES physical and cyber systems for active failure prevention subject to specific reliability requirements. This research will enable the design of AES with assured reliability. It will accelerate the development of AES by reducing the reliability certification effort in the design process. Additional deliverables of this project include improvement of reliability and design-related curricula, research experiences for undergraduate and graduate students, and outreach initiatives for students and under-represented minorities.The overarching goal of this project is to create a new design paradigm shift from the conventional reliability-based design of engineered systems through passive reliability assurance to the model-based design of reliable AES to account for both passive reliability assurance at the population level and active failure prevention at the individual level. Personalized active failure prevention and autonomy set AES apart from conventional engineered systems and make current design under uncertainty methodologies inapplicable to AES. The research draws upon model-based design, statistics, machine learning, and optimization to fill the gap in the current methods. It will create innovations in reliability analysis by tackling the computational challenges through a first-passage method and co-simulations, while accounting for active failure prevention, along with innovations in design optimization by decoupling and decentralizing reliability-based co-design problems. The research has broad societal impacts by enabling the design of AES with active failure prevention with the consideration of reliability, which accelerates the development of next-generation reliable autonomous engineering systems to replace humans in safety-critical or dangerous mission/working environments. The integrated education plan involves curriculum improvement, engaging undergraduate students in research, dissemination of research results, and outreach to K-12 students and teachers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.