Abstract
We pose the maintenance planning for systems using probabilistic Remaining Useful Life (RUL) prognostics as a renewal reward process. Data-driven probabilistic RUL prognostics are obtained using a Convolutional Neural Network with Monte Carlo dropout. The maintenance planning model is illustrated for aircraft turbofan engines. The results show that in the initial monitoring phase, the accuracy and sharpness of the RUL prognostics is relatively small. The maintenance of the engines is therefore scheduled far in the future. As the usage of the engine increases, the accuracy of the prognostics improves, while the sharpness remains relatively small. As soon as the estimated probability of the RUL is skewed towards 0, the maintenance planning model consistently indicates it is optimal to replace the engines immediately, i.e., "now". This shows that probabilistic RUL prognostics support an effective maintenance planning of the engines, despite being imperfect with respect to accuracy and sharpness.
Original language | English |
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Title of host publication | Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023) |
Editors | Mario Brito, Terje Aven, Piero Baraldi, Marko Cepin, Enrico Zio |
Publisher | Research Publishing |
Pages | 1034-1041 |
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
- Predictive maintenance planning
- Probabilistic RUL
- prognostics
- Aircraft engines
- Renewal processes
- Convolutional neural network
- Monte Carlo dropout