A Similarity-based and Model-based Fusion Prognostics Framework for Remaining Useful Life Prediction

Abstract

In this work, a hybrid prognostic framework which interfaces a model-based prognostic method, namely particle filter, with a similarity-based prognostic method is proposed. The proposed framework consists of automatic determination of predication start point, sensor fusion, and prognostics steps that lead to accurate remaining useful life (RUL) estimations. This approach first applies the canonical variate analysis (CVA) approach for determining the prediction start time and constructing the prognostic health indicators (HIs). The similarity-based method is then employed together with the model-based particle filter (PF) algorithm to improve the predictive performance in terms of reducing the uncertainty of RUL and improving the prediction accuracy. The proposed framework can automatically construct HIs that are suitable for RUL prediction and offer higher prediction accuracy and lower uncertainty boundaries than traditional model-based PF methods. Our proposed approach is successfully applied on aircraft turbofan engines RUL prediction.