Sensor-Data-Driven Fusion Prognostic Framework for Complex Engineered Systems

Abstract

Complex engineered systems have complex system structures and competing failure mechanisms, which means that neither model-based or data-driven approaches are suitable for conducting health state prognostics. Prognostics and health management (PHM) has been found to be an effective tool for advance failure warnings, system health status assessments, and predictive maintenance. To improve system health state forecasting accuracy, this paper developed a PHM-based fusion prognostic framework that strategically integrated the strengths of a model-based particle filter approach and a data-driven prognostics approach and obviated the respective limitations. An entropy-based fusion prognostics model was employed to fuse the particle filter prognostics results generated at different times and obtain more accurate prognostics results. Using sensor data sets from the NASA Ames Research Center, the developed fusion prognostics framework was then employed to estimate the health state of an aircraft gas turbine engine. The experimental results demonstrated that the proposed fusion prognostics framework was an effective prognostics tool and could achieve more accurate, robust health state prognostics.