Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction

Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction: Adaptable Time Window and Sub-Network Training

Borst, Nick (TU Delft Aerospace Engineering)

Verhagen, W.J.C. (mentor)
Santos, Bruno F. (graduation committee)
Zarouchas, D. (graduation committee)

Delft University of Technology

Aerospace Engineering

2020-08-31

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.

Prognostics
RUL prediction
Regression model
CNN
LSTM
Ensemble Method
CMAPSS

http://resolver.tudelft.nl/uuid:67f63bb4-85a6-4570-bd03-42e1325798ef

Student theses

master thesis

© 2020 Nick Borst