TY  - JOUR
T1  - Supervised Domain Adaptation for Remaining Useful Life Prediction of Aircraft Engines Using AdaBoost-LSTM
AU  - Seo, Seunghwan 
AU  - Hwang, Jungwoo 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.2.177
KW  - Remaining Useful Life Prediction
KW  - Domain Adaptation
KW  - AdaBoost-LSTM
KW  - Transfer Learning
AB  - 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.