  The 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 multi-layer perceptron and long short-term memory
layers (LSTM) to predict the RUL. 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. 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 score 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.
