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Xin Wang, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng and Bo Zheng
Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on t...
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Xiaofeng Liu, Liuqi Xiong, Yiming Zhang and Chenshuang Luo
Turbofan engines are known as the heart of the aircraft. The turbofan?s health state determines the aircraft?s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable o...
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Genane Youness and Adam Aalah
Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available ...
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Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi and Mohamed Benbouzid
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be c...
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Phattara Khumprom, David Grewell and Nita Yodo
Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM)...
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