By Topic

Early fault identification of aero-engine based on support vector machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wang Zhongsheng ; Sch. of Aeronaut., Northwestern Polytech. Univ., Xian ; Li Shuang

We proposed a new method of aero-engine early fault intelligent diagnosis which combined with stochastic resonance, wavelet packet analysis and support vector machine. This method can effectively extract the early fault feature of aero-engine and it can fast identify the early faults. At first, we use the principle of stochastic resonance to zooms the early weak fault feature signals and amplify fault features. Then, we make use of multi-resolution analysis characteristic of wavelet packet to extract the early fault feature vectors. At last, the feather vector is inputted to a classifier which is constructed by support vector machines and carries on identification of the early faults. The results shown that its effect of classification identification is well and it is effective to identify early fault in strong noise.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008