NDLI: Multiple defect diagnostics of gas turbine engine using SVM and RCGA-based ANN algorithms

Content Provider	Springer Nature Link
Author	Kim, Youngho Jang, Junyoung Kim, Wanjo Roh, Tae Seong Choi, Dong Whan
Copyright Year	2012
Abstract	An artificial neural network (ANN) based on the real coded genetic algorithm (RCGA) has been used with the support vector machine (SVM) for developing the defect diagnostics of the turbo-shaft engine of an aircraft. Nonlinearity increases due to the ascending number of input data in the off-design region. If the ANN algorithm is used by itself to determine defects under this condition, the possibility of falling in the local minima becomes high because of the large amount of learning data. To solve this problem, the expanded multi-class SVM has been used to reduce nonlinearity of input data. The RCGA, which is effective to search the global minima, has been applied to the ANN algorithm to obtain the magnitude of defects. As results, the number of learning data has been decreased and convergence and accuracy have been improved.
Starting Page	1623
Ending Page	1632
Page Count	10
File Format	PDF
ISSN	1738494X
Journal	Journal of Mechanical Science and Technology
Volume Number	26
Issue Number	5
e-ISSN	19763824
Language	English
Publisher	Korean Society of Mechanical Engineers
Publisher Date	2012-05-20
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Diagnostics Turbo-shaft engine SVM ANN RCGA Industrial and Production Engineering Vibration, Dynamical Systems, Control Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Mechanical Engineering

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