GOALI: Adaptive Degradation-Based Prognosis with Application to Vehicular Electrical Systems

该奖项的研究目标是使用基于实时性能/条件的传感器信号来表征多组件系统中组件与组件之间的退化相互作用，并使用此表征来改善复杂工程系统的基于传感器的性能。 在一个给定的系统中，用于对组件的相互依赖性进行建模的一般方法传统上集中于研究组件的故障对剩余的幸存组件的影响。 相比之下，本项目的方法通过关注系统组件逐渐和部分退化的影响，而不是其故障的影响，在更基本的层面上解决了这一挑战。 这将通过一个组合的随机和统计建模框架来实现，该框架将用于为具有相互依赖的降解过程的组件开发自适应预测模型。 该项目的成果包括具有预测算法的软件、研究结果的文档、工业平台上的验证以及工程学生教育。该项目的成功将对改善人类安全，降低美国汽车行业的维护和保修成本产生直接影响。 具体而言，GOALI项目是与通用汽车公司密切合作进行的。 它旨在将预测方法应用于车载发电和存储（EPGS）系统的关键部件。 这项研究的结果也将使许多其他产业部门受益，包括航空业、发电业、制造业和服务业领域。 研究议程将作为两个博士学位的博士论文的基础。学生，为他们提供丰富的培训经验，结合理论和工业工作以及实习机会。 研究结果将纳入预测学研究生课程。 传播将包括向学术界以及工业研讨会和讲习班作会议介绍。

The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to use real-time performance-/condition-based sensor signals to characterize component-to-component degradation interactions in multi-component systems, and use this characterization to improve sensor-based prognostics of complex engineering systems. The general approach used for modeling component interdependencies within a given system has traditionally focused on investigating the effects that a component's failure has on the remaining surviving components. In contrast, this project's approach addresses this challenge at a much more fundamental level by focusing on the effects of gradual and partial degradation of system components rather than the effects of their failures. This will be achieved through a combined stochastic and statistical modeling framework, which will be used to develop adaptive prognostic models for components with interdependent degradation processes. Deliverables for this project include a software with prognostic algorithms, documentation of research results, validation on industrial platform, and engineering student education. The success of this project will have a direct impact on improving human safety, and reducing maintenance and warranty costs of the American automotive industry. Specifically, this GOALI project is conducted in close collaboration with General Motors. It aims to apply the prognostic methods on key components of the Vehicular Electric Power Generation and Storage (EPGS) system. Findings of this research will also benefit many other industrial sectors, including the airline industry, the power generation industry, manufacturing sector, and domains of the service sector. The research agenda will serve as the foundation for the doctoral dissertations of two Ph.D. students, providing them with a rich training experience that combines theoretical and industrial work as well as internship opportunities. Research findings will be incorporated in a Prognostics graduate course. Dissemination will include conference presentations to the academic communities as well as industrial seminars and workshops.