Aeroengine state prediction based on generative adversarial networks and deep learning Online publication date: Wed, 27-Oct-2021
by Qiang Fu; Huawei Wang
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 15, No. 4, 2021
Abstract: The artificial intelligence technology represented by deep learning provides the possibility to make a comprehensive characterisation of the aeroengine state from state monitoring information. The premise of these algorithms is based on big data. First, this paper applies the generative adversarial networks to generate aeroengine condition monitoring data to expand data volume. Experimental results confirm that the generated data can reflect the regularity of the original monitoring data after a large number of network training iterations. Second, the deep learning algorithm is employed to predict the aeroengine status of the monitoring data and its generated data. Prediction accuracy is compared with the traditional neural network prediction method, which demonstrates the effectiveness of the deep learning prediction and the combination of the generative adversarial networks and deep learning. This aspect can solve the problem of limited data volume.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Aided Engineering and Technology (IJCAET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook