Learning Representations with End-to-End Models for Improved Remaining Useful Life Prognostic

Abstract

Remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life provides decision-makers with valuable information to adopt an appropriate maintenance strategy to maximize equipment utilization and avoid costly breakdowns. In this work, we propose an end-to-end deep learning model based on dense layers and long short-term memory layers (LSTM) to predict the RUL. After normalization of all data, inputs are fed directly to several dense layers for feature learning, then to an LSTM layer to add the notion of time, and finally to other dense layers for RUL prediction. The proposed architecture is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity with respect to other recently proposed models, the model developed outperforms them with a significant decrease in the competition score and in the root mean square error between the predicted and the gold value of the RUL. In this paper, we will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.