Abstract
Health indicators are crucial to assess the health of complex systems. In recent years, several studies have developed data-driven health indicators using supervised learning methods. However, due to preventive maintenance, there are often not enough failure instances to train a supervised learning model, i.e., the data is unlabelled with an unknown actual Remaining Useful Life (RUL). In this paper, we therefore propose an unsupervised learning model to construct a health indicator for an aircraft system. The considered system is operated under highly-varying operating conditions. We train a Convolutional Neural Network (CNN) to predict the sensor measurements from the operating conditions. We train this neural network solely with the sensor measurements of just-installed, non-degraded systems. The CNN therefore learns the normal range of the sensor measurements, given the operating conditions, for non-degraded systems only. For a degraded system, the predicted sensor measurements deviate from the actual sensor measurements. Based on the prediction errors, we construct a health indicator for the aircraft system. We apply this approach to develop a health indicator for the aircraft turbofan engines of dataset DS02 and DS06 of N-CMAPSS. The resulting health indicators have a high prognosability of 0.91 for DS02 and of 0.83 for DS06, a mean trendability of 0.86 for DS02 and of 0.87 for DS06, and a mean monotonicity of 0.31 for DS02 and of 0.33 for DS06, and can thus be used to make a reliable assessment of the system's health.
Original language | English |
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Title of host publication | Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023) |
Editors | Mario P. Brito, Terje Aven, Piero Baraldi, Marko Cepin, Enrico Zio |
Publisher | Research Publishing |
Pages | 3066-3073 |
Number of pages | 8 |
ISBN (Electronic) | 978-981-18-8071-1 |
DOIs | |
Publication status | Published - 2023 |
Event | The 33rd European Safety and Reliability Conference (ESREL 2023): The Future od Safety in a Reconnected World - University of Southampton, Southampton, United Kingdom Duration: 3 Sept 2023 → 7 Sept 2023 https://www.esrel2023.com/ |
Conference
Conference | The 33rd European Safety and Reliability Conference (ESREL 2023) |
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Abbreviated title | ESREL 2023 |
Country/Territory | United Kingdom |
City | Southampton |
Period | 3/09/23 → 7/09/23 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Health indicator
- few failure instances
- unsupervised learning
- varying operating conditions
- highfrequency data
- Convolutional Neural Network