Supervised Domain Adaptation for Remaining Useful Life Prediction of Aircraft Engines Using AdaBoost-LSTM 


Vol. 49,  No. 2, pp. 177-185, Feb.  2024
10.7840/kics.2024.49.2.177


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  Abstract

Along with the advent of high-quality deep learning algorithms, several methods have been published for the domain adaptation (DA) problem on remaining useful life. Most of them are unsupervised DA methods and popular adversarial approaches are known to have best performance among them. But, we have found out that adversarial approaches have an unstable problem that is the performance critically depends on the starting weights of the deep-learning networks. Furthermore, unsupervised DA methods could get a limited performance improvement if domain shift is larger than some extent. This paper proposes a supervised DA method based on AdaBoost with Long Short-Term Memory (LSTM) as base estimators. The proposed approach is effective when target domain data is much smaller than source domain data. On a publically accessible dataset, the proposed methodology is tested, and when compared to previous unsupervised domain adaption prediction methods, it reaches state-of-the-art prediction performance.

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[IEEE Style]

S. Seo and J. Hwang, "Supervised Domain Adaptation for Remaining Useful Life Prediction of Aircraft Engines Using AdaBoost-LSTM," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 177-185, 2024. DOI: 10.7840/kics.2024.49.2.177.

[ACM Style]

Seunghwan Seo and Jungwoo Hwang. 2024. Supervised Domain Adaptation for Remaining Useful Life Prediction of Aircraft Engines Using AdaBoost-LSTM. The Journal of Korean Institute of Communications and Information Sciences, 49, 2, (2024), 177-185. DOI: 10.7840/kics.2024.49.2.177.

[KICS Style]

Seunghwan Seo and Jungwoo Hwang, "Supervised Domain Adaptation for Remaining Useful Life Prediction of Aircraft Engines Using AdaBoost-LSTM," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 177-185, 2. 2024. (https://doi.org/10.7840/kics.2024.49.2.177)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880