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: $ 6.7万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752250
• 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，包括安全性关键和网络物理系统（CPS），都需要在长时间内可靠，有效，安全，安全地执行，而不会在其性能中进行任何中断或退化。这一要求通常源于安全，保障，经济学或环境原因。 航空航天，汽车，制造，自主空间以及水下车辆和结构，（核）电源和通信系统；化学过程控制和盟友行业；水资源；存储和智能网格就是一些例子。但是，事件（例如衰老）导致绩效降解，工具或组件的完全失败；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，通过智能能力不断监控系统健康的智能能力提供了故障/攻击弹性；检测并对任何可能的故障或攻击的严重性进行排名； 对于所有上述系统，通过自我康复来隔离，预测和适应它们是非常理想的功能。所提出的研究位于控制，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汇处，将解决一些基本知识的缺失核心，这些核心将为监控复杂系统/CP的健康和正常功能的方式铺平道路，并将防御不良影响，例如通过对组成的攻击和侵害攻击，并侵害了不良效果。该研究重点是首先为系统/CP提供复杂的功能，包括故障（或攻击）检测以及隔离（FDI或ADI），之后，估计，预后和住宿（EPA）之后。 这是无需昂贵的硬件和基于分析冗余的方法而实现的。电子和人工智能正在通过革命性的转型使汽车行业通过轮胎压力监测器传感器，反锁制动或GPS系统对汽车的最新发作，这使该行业提醒了该行业的一些新兴挑战，他们正在寻求帮助大学寻求帮助。鉴于该行业在安大略省和密歇根州的需求，申请重点领域是电池诊断和预后，安全问题，安全问题，FDI/ADI和EPA在连接的自动驾驶汽车（CAV）系统中。申请人在三十五年前与工业，政府和其他大学组织的稳定记录，具有超过35年的研究和协作。许多高素质的学生和研究人员已经在他的实验室接受了培训，并在加拿大和世界各地成功雇用。这项研究对许多加拿大行业具有价值，并且有很大的商业化机会。