Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines

Marios Kefalas, Mitra Baratchi, Asteris Apostolidis, Dirk Van Den Herik, Thomas Back

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419703
DOIs
Publication statusPublished - 7 Jun 2021
Externally publishedYes
Event2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021 - Detroit, United States
Duration: 7 Jun 20219 Jun 2021

Publication series

Name2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021

Conference

Conference2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021
CountryUnited States
CityDetroit
Period7/06/219/06/21

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