DocumentCode :
2481407
Title :
Early fault classification identification and fault self-recovery on aero-engine
Author :
Wang, Zhongsheng ; Jiang, Hongkai ; Xu, Yiyan
Author_Institution :
Sch. of Aeronaut., Northwest Polytech. Univ., Xian
fYear :
2008
fDate :
25-27 June 2008
Firstpage :
1935
Lastpage :
1939
Abstract :
In order to increase the safety of aero-engine and solve the fault sample shortage in aero-engine fault diagnosis, we put forward a new method. This method can efficiently identify the early fault of aero-engine and it has function of fault self-recovery. Fault classification identification and fault self-recovery are adopted. It is combined with the Stochastic Resonance (SR), Wavelet Packet Analysis (WPA) and Support Vector Machine (SVM) and the fault self-recovery method of multi-modules cooperation is used. In this paper, the basic composition of system, the way of weak fault feature zoom, the extraction of fault feature vector, the principle of fault classification, the structure of multi-faults classifier and realization of fault self-recovery are studied. It provides a new technical way to increase ability on identification and protection for aero-engine early fault. The results show that the method can effectively identify the aero-engine early fault in shortage of fault samples numbers and it can realize the fault self-recovery of aero-engine.
Keywords :
aerospace computing; aerospace engines; fault diagnosis; feature extraction; stochastic processes; support vector machines; wavelet transforms; aero-engine fault diagnosis; early fault classification identification; fault feature vector extraction; fault self-recovery; fault self-recovery method; multimodules cooperation; stochastic resonance; support vector machine; wavelet packet analysis; Fault diagnosis; Feature extraction; Protection; Safety; Stochastic resonance; Strontium; Support vector machine classification; Support vector machines; Wavelet analysis; Wavelet packets; aero- engines; classification identification; early fault; fault self-recovery; stochastic resonance; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
Type :
conf
DOI :
10.1109/WCICA.2008.4593220
Filename :
4593220
Link To Document :
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