@inproceedings{c655f3e301a8425d89eca155fa46e829,
title = "Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines",
abstract = "Remaining useful life (RUL) of an asset or system is defined as the length from the current time and operating state to the end of the useful life. It is of paramount importance for safety-critical industries such as aviation and lies in the heart of prognostics and health management (PHM). This paper investigates the usage of automated machine learning (AutoML) for RUL estimation, based on using classical machine learning algorithms for regression. The data is pre-processed by extracting statistical features from expanding windows of the signal in order to uncover the degradation that has been accumulating from the early life of the system or after an overhaul. We evaluate our methodology on the widely-used C-MAPSS dataset and compare our approach to the state-of-the-art deep neural networks (DNNs) and classical machine learning algorithms. The experimental results show that AutoML outperforms or is comparable to traditional machine learning techniques and standard neural networks, while being outperformed by specifically designed neural networks on datasets with multiple fault mode and operating conditions. These results show that with the correct pre-processing automated machine learning is able to accurately estimate the RUL, which implies that such approaches can be industrially deployed. ",
keywords = "Machine Learning, Remaining Useful Life, Aircraft Engines, Life Estimation, Automated Machine Learning, Useful Life",
author = "Marios Kefalas and Mitra Baratchi and Asteris Apostolidis and {van den Herik}, Dirk and Thomas B{\"a}ck",
note = "Funding Information: *This work is part of the research programme Smart Industry SI2016 with project name CIMPLO and project number 15465, which is partly financed by the Netherlands Organisation for Scientific Research (NWO). ; 2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021 ; Conference date: 07-06-2021 Through 09-06-2021",
year = "2021",
month = jun,
day = "7",
doi = "10.1109/icphm51084.2021.9486549",
language = "English",
isbn = "9781665429962 (PoD)",
series = "2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021",
address = "United States",
}