Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning-Reference-Cited by-同舟云学术

Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning

Published:2022-05 Issue: Volume:171 Page:108809
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Candon Michael,Esposito Marco,Fayek Haytham^ORCID,Levinski Oleg,Koschel Stephan^ORCID,Joseph Nish,Carrese Robert,Marzocca Pier

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference53 articles.

1. Pattern recognition based on time series analysis using vibration data for structural health monitoring in civil structures;Mustapha;Electron. J. Struct. Eng.,2015

2. Failure prediction of ±55° glass/epoxy composite pipes using system identification modelling;Yi;J. Phys. Conf. Ser.,2012

3. Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction;Sarwar;Energy,2017

4. Remaining useful life prediction of gas turbine engine using autoregressive model;Ahsan,2017

5. A Neural Network Prototype for Predicting F-14b Strains at the Bl Longeron;Hoffman,1992

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