Prediction Remaining Useful Life of Aircraft Turbofans Using Transfer Learning Based CNN-LSTM 


Vol. 13,  No. 12, pp. 700-709, Dec.  2024
https://doi.org/10.3745/TKIPS.2024.13.12.700


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  Abstract

The objective of research in the field of prognostics and health management is to predict the Remaining Useful Life of aircraft engines, a critical component of analysis within this domain. Nevertheless, there are difficulties in acquiring dependable failure information, and the limited availability of defect data hinders the development of predictive models. Current data augmentation techniques are utilized to enhance the insufficient defect data; however, the heuristic approaches might oversimplify the data characteristics, ultimately decreasing predictive accuracy. This study suggests a hybrid model that combines Transfer Learning, specifically integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The hybrid CNN-LSTM model integrates the CNN’s feature extraction capabilities with the LSTM’s long-term time series learning capacity, facilitating the representation of intricate dynamic characteristics and temporal fluctuations in aircraft engine sensor data. The performance of predictive techniques is enhanced by applying data learned from various source domains to target domain data through transfer learning. The results obtained by applying this model to the C-MAPSS aircraft engine simulator dataset developed by the National Aeronautics and Space Administration (NASA) corroborate the idea that employing a pre-trained model through transfer learning improves predictive accuracy in comparison to the standard mixed model. Furthermore, the proposed model demonstrates improved predictive abilities when compared to various leading predictive models in the PHM field.

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[IEEE Style]

K. J. Min, K. H. Woo, C. Y. Ki, K. G. Hyuk, A. S. Yeon, K. H. Kee, "Prediction Remaining Useful Life of Aircraft Turbofans Using Transfer Learning Based CNN-LSTM," The Transactions of the Korea Information Processing Society, vol. 13, no. 12, pp. 700-709, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.12.700.

[ACM Style]

Kim Jeong Min, Kang Hyeon Woo, Cho Young Ki, Kwon Gi Hyuk, An Seo Yeon, and Kim Hun Kee. 2024. Prediction Remaining Useful Life of Aircraft Turbofans Using Transfer Learning Based CNN-LSTM. The Transactions of the Korea Information Processing Society, 13, 12, (2024), 700-709. DOI: https://doi.org/10.3745/TKIPS.2024.13.12.700.

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