A Stacked Deep Convolutional Neural Network to Predict the Remaining Useful Life of a Turbofan Engine

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Published Nov 24, 2021
David Solís-Martín Juan Galán-Páez Joaquín Borrego-Díaz

Abstract

This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodology was ranked in the third place of the 2021 PHM Conference Data Challenge.

How to Cite

Solís-Martín, D., Galán-Páez, J., & Borrego-Díaz, J. (2021). A Stacked Deep Convolutional Neural Network to Predict the Remaining Useful Life of a Turbofan Engine. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.3110
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Keywords

Deep Learning, RUL, Convolutional Networks, Recurrent Networks, Auto-encoder, Turbofan engine

Section
Data Challenge Winners