A Hybrid Framework Combining Data-level Fusion and Model-based Models for Remaining Useful Life Prediction

Abstract

In this work, a hybrid prognostic framework is put forward to predict remaining useful life (RUL) through the fusion of multiple degradation-based sensor data. This approach is tested on measurements obtained from an industrial centrifugal pump. The prognostic framework consists of determination of predication start point, sensor selection and fusion, and prognostics steps that lead to accurate RUL prediction. This approach first applies the canonical variate analysis (CVA) method to the pump for determining the prediction start time. We also present an approach for constructing a single-valued prognostic health indicator through the fusion of fault root-case variables. Moreover, the particle filter (PF) algorithm is deployed to improve the metabolism grey forecasting model (MGFM) to realize the prediction of both RUL and uncertainties.