An Integrated Deep Learning-Based Data Fusion and Degradation Modeling Method for Improving Prognostics

Accurate prognostics are crucially important to prevent unexpected failures in industrial and service systems. This process aims to monitor the degradation status of units and predict their remaining useful lifetime (RUL) by analyzing the data collected from multiple sensors. Existing studies for pr...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 21; H. 2; S. 1713 - 1726
Hauptverfasser:	Wang, Di, Liu, Kaibo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Aging aircraft Aircraft engines Algorithms Artificial neural networks Atmospheric modeling Back propagation Back propagation networks Backpropagation algorithms Data integration Data models Deep learning Degradation Gas turbine engines Health and safety Health index construction Indexes integrated backpropagation parameter estimation algorithm Lifetime estimation Machine learning Multisensor fusion Parameter estimation Predictive models remaining useful lifetime prediction Sensor fusion Sensor phenomena and characterization Sensors Statistical methods Statistical models
ISSN:	1545-5955, 1558-3783
Online-Zugang:	Volltext
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