Proposing end-to-end deep learning models to predict the Remaining Useful Life of equipment
Ιn the publication ‘Learning Representations with End-to-End Models for Improved Remaining Useful Life Prognostic’ Alaaeddine Chaoub, Alexandre Voisin, Christophe Cerisara and Benoit Iung, propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the Remaining Useful Life (RUL) of equipment.
As they explain, the RUL of equipment is defined as the ‘duration between the current time and the time when it no longer performs its intended function’. 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. As the authors explain, after normalization of all data, inputs are fed directly to an MLP layers for feature learning, then to an LSTM layer to capture temporal dependencies, and finally to other MLP layers for RUL prognostic.
To be more specific, this 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 score between the predicted and the gold value of the RUL.
In this paper, you will read how the proposed end-to-end model is able to achieve such good results and will be furthermore compared to other deep learning and state-of-the-art methods.