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

GOALI学术联络机会奖的研究目标是使用基于实时性能/条件的传感器信号来描述多组件系统中组件到组件的退化交互作用，并使用这种特征来改进基于传感器的复杂工程系统的预测。用于对给定系统中的组件相互依赖关系进行建模的一般方法传统上侧重于调查组件故障对其余幸存组件的影响。相比之下，该项目的方法通过关注系统部件逐渐和部分降级的影响，而不是其故障的影响，在更根本的层面上解决了这一挑战。这将通过一个综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退化过程的组成部分开发自适应预测模型。该项目的可交付成果包括一个带有预测算法的软件、研究结果文档、在工业平台上的验证以及工程专业学生的教育。该项目的成功将对改善人类安全、降低美国汽车业的维护和保修成本产生直接影响。具体地说，这个Goali项目是在与通用汽车的密切合作下进行的。其目的是将预测方法应用于车载发电与储能系统的关键部件。这项研究的结果也将使许多其他工业部门受益，包括航空业、发电业、制造业和服务业领域。研究议程将作为两名博士生撰写博士论文的基础，为他们提供丰富的培训经验，将理论工作和工业工作结合起来，并提供实习机会。研究结果将被纳入预见学研究生课程。传播将包括对学术界的会议介绍以及工业研讨会和讲习班。

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.