Fault Early Recognition and Health Monitoring on Aeroengine Rotor System
Publication: Journal of Aerospace Engineering
Volume 28, Issue 2
Abstract
A new method is presented to improve the safety and reliability of an aeroengine rotor system (AERS) in early fault recognition and health monitoring, addressing the problems that fault samples are not sufficient and that early weak faults are not easy to recognize. First, the stochastic resonance system is used to refine the early weak feature signal so as to amplify the fault information. Second, the early fault features are extracted by multiresolution performance of wavelet packet analysis, and the fault characteristic vector can be constructed. Finally, the extracted eigenvectors import the support vector machine (SVM) classifier to carry on the fault recognition and then make use of intelligent monitor module to monitor early faults in AERS. Experimental results have shown that this method can not only early recognize faults in AERS but also monitor the fault online. It provides a new way to increase the safety of AERS and predict the sudden fault.
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Acknowledgments
This paper, as part of Projects 51075330 and 50675178, is supported by National Natural Science Foundation of China.
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Published In
Journal of Aerospace Engineering
Volume 28 • Issue 2 • March 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jan 17, 2012
Accepted: Oct 3, 2013
Published online: Oct 5, 2013
Discussion open until: Dec 3, 2014
Published in print: Mar 1, 2015
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