Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great performances for the RUL prediction. However, the con...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 68; H. 3; S. 2521 - 2531
Hauptverfasser:	Chen, Zhenghua, Wu, Min, Zhao, Rui, Guretno, Feri, Yan, Ruqiang, Li, Xiaoli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Attention mechanism Deep learning Feature extraction feature fusion handcrafted features Life prediction long short-term memory (LSTM) Machine learning machine remaining useful life (RUL) prediction Mechanical systems Prediction algorithms Predictive models Prognostics and health management prognostics and health management (PHM) Time series analysis Useful life
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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