@inproceedings{034e95fc049244dc8e89615f01f6bc96,
title = "Sequence Adaptation Adversarial Network for Remaining Useful Life Prediction Using Small Data Set",
abstract = "Data-driven intelligent method has shown superior performance in remaining useful life (RUL) prediction. However, the model training is difficult due to the limited degradation data. To address the challenges of small data set, a Sequence Adaptation Adversarial Network (SAAN) is proposed in this paper. SAAN could expand training data with auxiliary set by sequence domain adaption. We verify the proposed method with C-MAPSS dataset. By comparing with the literature methods, results show SAAN could significantly improve the accuracy of RUL prediction under small data set, and also keeps a competitive performance on sequence life prediction.",
keywords = "RUL prediction, aero-engine, deep learning, domain adaption",
author = "Haixin Lv and Jinglong Chen and Tongyang Pan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 18th IEEE International Conference on Industrial Informatics, INDIN 2020 ; Conference date: 21-07-2020 Through 23-07-2020",
year = "2020",
month = jul,
day = "20",
doi = "10.1109/INDIN45582.2020.9442160",
language = "英语",
series = "IEEE International Conference on Industrial Informatics (INDIN)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "115--118",
booktitle = "Proceedings - 2020 IEEE 18th International Conference on Industrial Informatics, INDIN 2020",
}