Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction

Borst, N.G.

Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction

Adaptable Time Window and Sub-Network Training

Master thesis (2020)

Authors

N.G. Borst Aerospace Engineering

Contributors

Wim Verhagen Air Transport & Operations - Aerospace Engineering (mentor)

Bruno F. Lopes dos Santos Air Transport & Operations - Aerospace Engineering (graduation committee member)

Dimitrios Zarouchas Structural Integrity & Composites - Aerospace Engineering (coach)

Faculty

Aerospace Engineering, Aerospace Engineering

CNN LSTM Regression model Ensemble Method Prognostics RUL prediction CMAPSS

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Published Date

31-08-2020

Language

English

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Faculty

Aerospace Engineering

Abstract

Estimating the RUL (Remaining Useful Life) of machinery is a useful tool for maintenance and performance operations. This results in lower costs, improved safety and operational improvements.
This paper proposes two adaptations to the CNN-LSTM network provided by Li et al. \cite{Li2019APrediction}, as well as exploring reproducibility, accuracy and sensitivity of the original DAG (Directed Acyclic Graph) network. The network at hand is an ensemble network combining LSTM and CNN neural networks to provide an accurate regression RUL prediction using the NASA CMAPSS dataset \cite{NasaNasaReprository}.
The Adaptable Time Window (ATW) adaptation increases the amount of time cycles that can be predicted and increases the accuracy, allowing for earlier predictions and better RUL predictions. Allowing state-of-the-art predictions accuracy for complex datasets. The Sub-network training adaptions did not surpass the accuracy of the original network with the current implementation settings, however is promising for further research.

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