Prediction of Failed Sensor Data Using Deep Learning Techniques for Space Applications-Reference-Cited by-同舟云学术

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Prediction of Failed Sensor Data Using Deep Learning Techniques for Space Applications

Published:2022-09-27 Issue:3 Volume:128 Page:1941-1962
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Das Renjith,Christopher A. Ferdinand

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11277-022-10027-2.pdf

Reference28 articles.

1. Zhao, Y.-P., Wang, J.-J., Li, X.-Y., Peng, G. J., & Yang, Z. (2020). Extended least squares support vector machine with applications to fault diagnosis of aircraft engine. ISA Transactions, 97, 189–201.

2. Altay, A., Ozkan, O., & Kayakutlu, G. (2014). Prediction of aircraft failure times using artificial neural networks and genetic algorithms. Journal of Aircraft, 51(1), 47–53.

3. Simon, Donald, L. and Rinehart, A.W. (2016) Sensor selection for aircraft engine performance estimation and gas path fault diagnostics. Journal of Engineering for Gas Turbines and Power, 138(7).

4. Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604–612.

5. Carbonneau, R., Kersten, G. E., & Vahidov, R. (2008). Predicting opponent’s moves in electronic negotiations using neural networks. Expert Systems with Applications, 34(2), 1266–1273.

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