Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

The vibration signals of faulty bearings under non-stationary conditions are inherently multi-component and time-varying, which presents a challenge for effective fault diagnosis. Considering the vibration characteristics of rolling bearings under non-stationary conditions and taking advantage of the Fractional Fourier Transform (FRFT), a novel diagnosis method based on the hypothesis-based FRFT has been proposed to separate the fault components. First, the fault characteristic frequencies (FCFs) are extracted from the time-frequency representation of the vibration signals, and the Vold-Kalman filtering is employed to eliminate the influence of noises and other interference components. Subsequently, the fractional feature model is constructed to obtain speed information by the hypothesis approach, whose central idea is that the rotational frequency (RF)-related frequencies under different fault types are estimated, based on the extracted FCFs and the fault characteristic orders. Finally, fault diagnosis is completed by the RF-related peaks in the final spectrum. The method eliminates the need for rotational speed measurement devices and angular resampling. Simulation and experiment estimation results show that the hypothesis-based FRFT method can accurately locate fault characteristic components of bearings under non-stationary conditions.