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Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine
Joint Authors
Yang, Xinyi
Lin, Xuesen
Jiang, Keyi
Shen, Wei
Wang, Yonghua
Pang, Shan
Source
International Journal of Aerospace Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-26
Country of Publication
Egypt
No. of Pages
10
Abstract EN
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.
American Psychological Association (APA)
Yang, Xinyi& Pang, Shan& Shen, Wei& Lin, Xuesen& Jiang, Keyi& Wang, Yonghua. 2016. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine. International Journal of Aerospace Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1105035
Modern Language Association (MLA)
Yang, Xinyi…[et al.]. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine. International Journal of Aerospace Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1105035
American Medical Association (AMA)
Yang, Xinyi& Pang, Shan& Shen, Wei& Lin, Xuesen& Jiang, Keyi& Wang, Yonghua. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine. International Journal of Aerospace Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1105035
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1105035