Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017, Article ID 9726529, 8 pages
Research Article

Aeroengine Fault Diagnosis Using Optimized Elman Neural Network

Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China

Correspondence should be addressed to Jiangbo Huang; moc.qq@6451044361

Received 8 June 2017; Accepted 22 November 2017; Published 19 December 2017

Academic Editor: Alessandro Gasparetto

Copyright © 2017 Jun Pi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new Elman Neural Network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) is introduced in this paper. According to the root mean square error, QAPSO is used to select the best weights and thresholds of the ENN in training samples. The optimized neural network is applied to aeroengine fault diagnosis and is compared with other optimized ENN, original ENN, BP, and Support Vector Machine (SVM) methods. The results show that the QAPSO-ENN is more accurate and reliable in the aeroengine fault diagnosis than the conventional neural network and other ENN methods; QAPSO-ENN has great diagnostic ability in small samples.