RUL prediction based on a new similarity-instance based approach

Prognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety, reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the Remaining Useful Life (RUL) of a degrading component/ system, i.e. to predict the time after which a component/system will no longer be able to meet its operating requirements. This RUL prediction is a challenging task that requires special attention when modeling the prognostics approach. In this paper, we proposes a RUL prediction approach based on Instance Based Learning (IBL) with an emphasis on the retrieval step of the latter. The method is divided into two steps: an offline and an online step. The purpose of the offline phase is to learn a model that represents the degradation behavior of a critical component using a history of run-to-failure data. This modeling step enables us to construct a library of health indicators (HI´s) from run-to-failure data which are then used online to estimate the RUL of components at an early stage of life, by comparing their HI´s to the ones of the library built in the offline phase. Our approach makes use of a new similarity measure between HIs. The proposed approach was tested on real turbofan data set and showed good performance compared to other existing approaches.