Abstract
Predictive Maintenance (PdM) plays an important role in detecting potential problems and preventing unexpected equipment failures in the industrial area. Transport systems represent another application domain where PdM could lead to higher availability and lower maintenance costs. In this paper, we propose a machine learning approach to predict the Remaining Useful Life (RUL) of turbofan units in aircraft.
Originalsprache | Englisch |
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Seitenumfang | 2 |
Publikationsstatus | Veröffentlicht - 2017 |
Veranstaltung | IAUP Triennial Conference - Wien, Österreich Dauer: 5 Juli 2017 → 8 Juli 2017 |
Konferenz
Konferenz | IAUP Triennial Conference |
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Land/Gebiet | Österreich |
Ort | Wien |
Zeitraum | 05.07.2017 → 08.07.2017 |
Schlagwörter
- Predictive Maintenance
- Datastream Analysis
- Sliding Window
- Symbolic Regression
- Ensemble Modeling