Bearing weak fault feature extraction under time-varying speed conditions is a challenging task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot work well for detecting time-varying fault characteristic frequency (FCF) harmonics of the bearing due to the limitation of time-frequency resolution and noise interference. As such, a novel frequency matching demodulation transform (FMDT) technique is developed by extending the generalized demodulation transform for bearing weak fault feature extraction and diagnosis under variable speeds. The main novelties of this article are concluded that: the FMDT can first estimate FCF under strong noise conditions by generating a series of demodulation parameters to match FCF with the criterion that the maximal peak can be captured at a specific frequency from the spectra of the demodulated signals, and then calculate rotational frequency (RF) harmonics; and the proposed FMDT-based method does not require the measured bearing RF, and can directly determine bearing fault type using the estimated FCF and RF harmonics. The effectiveness of the FMDT-based bearing fault diagnosis method is verified by both simulated and experimental results. Comparison analysis shows that the proposed method can generate much better results than the classic TFA-based ridge detection algorithms.

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.