GOALI: Integrated Signal Processing for Bearing Health Assessment and Life Prediction

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This Grant Opportunity for Academic Liaison with Industry (GOALI) provides funding for the development of a unified time-scale-frequency domain signal processing technique that is germane to the health assessment and time-to-failure prediction of rolling bearings. The project is aimed at bridging an existing gap between the state-of-the-art of signal processing research in the academia and current bearing condition monitoring practice on the factory floor, and will focus on establishing the theoretical foundation for an effective and efficient algorithm to estimate a defective bearing's damage status and remaining service life. The research will consists of numerical simulations in multiple domains, custom-designed experimental validation, and systematic analysis of historical data provided by the industrial partner of this project. The developed technique will be implemented in the form of a bearing assessment firmware, residing on a Digital Signal Processor platform, and installed in a handheld bearing data analyzer, suited for direct industry translation to bearing manufacturers and end-users.This research, if successful, will lead to improved capability of concurrent machine fault feature extraction in multiple domains. Such a technique will be essential to reducing costly and unexpected machine downtime, and improving product quality. In a broader context, the research will enhance existing curricula in manufacturing and mechanical engineering, create new, research-based educational materials, promote collaborations between a major public university and the bearing industry, and improve the overall education infrastructure at the PI's institution. Ultimately, by being able to more reliably predict the health status of a machine system and improve operation safety and productivity, the research has the potential to impact a wide range of manufacturing processes other than bearings.