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Generalized Vold–Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear.

Authors :
Zhao, Dezun
Cheng, Weidong
Gao, Robert X.
Yan, Ruqiang
Wang, Peng
Source :
IEEE Transactions on Instrumentation & Measurement; Feb2020, Vol. 69 Issue 2, p401-410, 10p
Publication Year :
2020

Abstract

Effective detection of multifaults in bearings and gears is a challenging issue in rotary machinery health monitoring. As such, a generalized Vold–Kalman filtering (GVKF)-based compound faults diagnosis method is presented in this paper. The technique includes four main steps: 1) a time–frequency ridge is separated from the time–frequency representation (TFR) of the vibration signal using a peak search method; 2) according to the time–frequency ridge, GVKF parameters corresponding to all the fault characteristic frequencies (FCFs) are estimated; 3) the fault feature components are obtained using the generalized demodulation transform (GDT) and the VKF with the GVKF parameters; and 4) the spectra obtained by the fast Fourier transform (FFT) are used to fault detection. The main contributions of the proposed method are as follows: 1) the influence of speed fluctuations and the unrelated harmonic components are removed through the integration of the GDT and the VKF and 2) the tachometerless GVKF parameters are defined and calculated to quantitatively detect different fault types, which avoids missed diagnosis and misdiagnosis. The proposed multifault diagnosis algorithm is verified by both simulation and experiment data. Comparison with other commonly used techniques has shown the advantage of the new method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189456
Volume :
69
Issue :
2
Database :
Complementary Index
Journal :
IEEE Transactions on Instrumentation & Measurement
Publication Type :
Academic Journal
Accession number :
141083288
Full Text :
https://doi.org/10.1109/TIM.2019.2903700