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

• Published in: IEEE transactions on automation science and engineering Vol. 21; no. 2; pp. 1713 - 1726
• Main Authors: Wang, Di, Liu, Kaibo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...