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.
Original language | English |
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Number of pages | 2 |
Publication status | Published - 2017 |
Event | IAUP Triennial Conference - Wien, Austria Duration: 5 Jul 2017 → 8 Jul 2017 |
Conference
Conference | IAUP Triennial Conference |
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Country/Territory | Austria |
City | Wien |
Period | 05.07.2017 → 08.07.2017 |
Keywords
- Predictive Maintenance
- Datastream Analysis
- Sliding Window
- Symbolic Regression
- Ensemble Modeling