Remaining useful life prediction based on intentional noise injection and feature reconstruction

Abstract

The accurate remaining useful life (RUL) prediction is the foundation of prognostics and health management (PHM). The accuracy of RUL prediction model depends on not only the quality and quantity of degradation feature but also the prediction model. In most of the existing deep-learning based RUL prediction models, noise is considered harmful and has to be removed. Further, the correlation among sensory measurements is ignored. However, noise can boost the prediction performance if judiciously used. This paper proposes a new RUL prediction method where noise is intentionally added into a long short-term memory (LSTM) network. Additionally, correlation analysis is conducted among the sensory measurements to construct new degradation features as the inputs of the LSTM network. Validation of the proposed method was carried out on the C-MAPSS aero-engine lifetime dataset. Finally, the proposed RUL prediction model is compared to other the-state-of-the-art techniques.