DocumentCode
2005463
Title
Fault diagnosis based on the multiple preset GFRF models
Author
Wei, Ruixuan ; Han, Chongzhao ; Wang, Xisheng ; Yan, Hongsen
Author_Institution
Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China
Volume
2
fYear
2002
fDate
8-11 July 2002
Firstpage
1506
Abstract
The fault diagnosis based on the nonlinear spectral analysis is a new method with wide application foreground. The online computation of the generalized frequency response functions (GFRF) is needed in the standard fault diagnosis method based on nonlinear spectral analysis. Because the realization of GFRF identification in frequency domain is difficult, the application of this method in engineering field is limited. For avoiding to estimate the current GFRF´s of the analyzed system in the fault diagnosis based on nonlinear spectral analysis, a new diagnosis method, which is based on the multiple preset GFRF model and the simplified GFRF identification algorithm, is presented in this paper. And this method has been used to diagnose the fault of the actual vehicle damping spring, the test results indicate that the presented method is efficient. This method can not only exploit the advantages of the GFRF model and the simplified identification algorithm, but also avoid estimating the GFRF online. Furthermore, its computation requirement is small, and its realization by using microprocessors is convenient.
Keywords
fault diagnosis; frequency response; identification; spectral analysis; GFRF; fault diagnosis; generalized frequency response functions; identification; nonlinear spectral analysis; signal analysis; vehicle damping; Algorithm design and analysis; Damping; Fault diagnosis; Frequency domain analysis; Frequency response; Microprocessors; Spectral analysis; Springs; Testing; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Fusion, 2002. Proceedings of the Fifth International Conference on
Conference_Location
Annapolis, MD, USA
Print_ISBN
0-9721844-1-4
Type
conf
DOI
10.1109/ICIF.2002.1020995
Filename
1020995
Link To Document