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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.