Towards resiliency through health monitoring, diagnosis, prognosis, and fault tolerance in complex and cyber-physical systems with applications to electrified and connected vehicles.

通过复杂网络物理系统的健康监测、诊断、预测和容错，并应用于电气化和互联车辆，实现弹性。

• 批准号: RGPIN-2018-04002
• 负责人: Saif, Mehrdad
• 金额: $ 3.35万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689381
• 关键词: Towards; resiliency; through; health; monitoring

Many complex engineering systems, including safety critical and cyber-physical systems (CPS), need to perform reliably, efficiently, safely and securely for extended periods of time without any interruption or degradation in their performance. This requirement often stems from safety, security, economics, or environmental reasons. Aerospace, automotive, manufacturing, autonomous space and underwater vehicles and structures, (nuclear) power and communication systems; chemical process control and allied industries; water resources; energy storage and smart grids are some examples. However, events (e.g. aging) leading to performance degradation, incipient or complete failure of instruments or components; and in recent years, cyber and terrorist attacks on critical infrastructures are inevitable. Therefore, failure/attack resiliency offered by intelligent ability to continuously monitor the health of systems; detect and rank the severity of any possible faults or attacks; and isolate, prognose, and accommodate for them through self healing, would be highly desirable functions for all aforementioned systems. ******The proposed research, which resides at the intersection of control, communications, machine learning, data mining, computational intelligence, big data, and cyber-physical systems, will tackle some missing core of fundamental knowledge that would pave the way towards monitoring the health and normal functioning of complex systems/CPS, and will guard them against undesirable effects such as aging of components, failures or malicious attacks and intrusions by adversaries. The research focus is on providing the system/CPS with sophisticated capabilities including Fault (or Attack) Detection, and Isolation (FDI or ADI) in the first instance, and, Estimation, Prognosis, and Accommodation (EPA) afterwards. This is achieved without costly hardware and through analytical redundancy based approaches. ******Electronics and artificial intelligence is taking the automotive industry through a revolutionary transformation, however, recent attacks on cars through tire pressure monitor sensor, anti-lock braking, or GPS system, have alerted the industry regarding some emerging challenges and they are looking to universities for help. Given the needs of the industry in Ontario and Michigan, the application focus area is on battery diagnosis & prognosis in electrified vehicles, as well as security issues, FDI/ADI, and EPA in connected autonomous vehicle (CAV) systems.******The applicant has a solid track record of over thirty five years of research and collaboration with industry, government, and other university organizations. Many highly qualified students and research personnel have been trained in his lab and are successfully employed in Canada and all over the world. The research is of value to many Canadian industries and has a high chance for commercialization.*****

许多复杂的工程系统，包括安全关键系统和网络物理系统（CPS），需要长时间可靠、高效、安全地运行，而不会出现任何中断或性能下降。这一要求通常源于安全、安保、经济或环境方面的原因。 例如，航空航天、汽车、制造业、自主空间和水下运载工具和结构、（核）动力和通信系统;化学过程控制和相关工业;水资源;能源储存和智能电网。然而，导致性能下降、仪器或部件初期或完全故障的事件（如老化）以及近年来对关键基础设施的网络和恐怖袭击是不可避免的。因此，由智能能力提供的故障/攻击弹性，以连续监视系统的健康状况;检测和排名任何可能的故障或攻击的严重性;以及通过自我修复来隔离、排除和适应它们，将是所有上述系统的高度期望的功能。** 拟议的研究位于控制，通信，机器学习，数据挖掘，计算智能，大数据和网络物理系统的交叉点，将解决一些缺失的基础知识核心，为监控复杂系统/CPS的健康和正常功能铺平道路，并将保护它们免受组件老化等不良影响，故障或恶意攻击以及对手的入侵。研究重点是为系统/CPS提供复杂的功能，包括故障（或攻击）检测和隔离（FDI或ADI），然后是估计，预测和调节（EPA）。 这是实现没有昂贵的硬件和通过分析冗余为基础的方法。** 电子和人工智能正在使汽车行业经历革命性的转变，然而，最近通过轮胎压力监测传感器，防抱死制动或GPS系统对汽车的攻击已经提醒行业注意一些新出现的挑战，他们正在寻求大学的帮助。考虑到安大略和密歇根州的行业需求，应用重点领域是电动汽车的电池诊断和预测，以及互联自动驾驶汽车（CAV）系统的安全问题、FDI/ADI和EPA。申请人拥有超过35年的研究和与行业，政府和其他大学组织合作的良好记录。许多高素质的学生和研究人员在他的实验室接受了培训，并在加拿大和世界各地成功就业。该研究对加拿大许多行业都有价值，并且有很高的商业化机会。