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.
|Number of pages||10|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Early online date||1 Apr 2019|
|Publication status||Published - 1 Feb 2020|
Zhao, D., Cheng, W., Gao, R., Yan, R., & Wang, P. (2020). Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement, 69(2), 401-410. . https://doi.org/10.1109/TIM.2019.2903700