Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics

This is the authors' abstract. We don't add key points for this paper.

The rapid development of sensing and computing technologies has enabled multiple sensors embedded in a system to simultaneously monitor the degradation status of an operation unit. This creates a data-rich environment for degradation modeling and prognostics that could potentially lead to an accurate inference about the remaining lifetime of the degraded unit. However, as data collected from multiple sensors are often correlated and each sensor data contains only partial information about the same degradation process, there is a pressing need to develop data fusion methodologies that can integrate the data from multiple sensors for better characterizing the stochastic nature of the degradation process. Unlike other existing data fusion methodologies that treat the fusion procedure and the degradation modeling as two separate tasks, this paper aims at solving these two challenging problems in a unified manner. Specifically, we develop a methodology to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves a degradation dataset of an aircraft gas turbine engine is implemented to numerically evaluate and compare the prognostic performance of the developed health index with existing literature.