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International Journal of Aerospace Engineering
Volume 2016 (2016), Article ID 7892875, 10 pages
Research Article

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

1Department of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China
2College of Information and Electrical Engineering, Ludong University, Yantai 264025, China

Received 1 June 2015; Accepted 13 October 2015

Academic Editor: Roger L. Davis

Copyright © 2016 Xinyi Yang 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 extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.