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

Dezuo Zhao, Weidong Cheng, Robert Gao, Ruqiang Yan, Peng Wang

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


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.
Original languageEnglish
Article number8678678
Pages (from-to)401-410
Number of pages10
JournalIEEE Transactions on Instrumentation and Measurement
Issue number2
Early online date1 Apr 2019
Publication statusPublished - 1 Feb 2020
Externally publishedYes


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