Time-varying metrics of cyclostationarity for bearing diagnostic

Ball bearings represent the most adopted solution to support rotating elements. Separated by the cage, the rolling elements are induced by the kinematics of the system to roll and accidentally slip on the rings. In working conditions the continuous contact of the elements leads to a wearing of the bearing surfaces. As a consequence, the early detection of faults represents an issue for modern diagnostic systems. The mathematical model of faulted rolling bearings has been extensively investigated in the last decades and it is widely accepted that a faulted bearing is subject to an unwanted slippery leading to a cyclostationary vibration signal. This paper presents a novel approach to the diagnosis of rolling bearings based on the statistical definition of cyclostationarity. In particular, various metrics have been devised to track the “cyclostationary signature” of the vibration signal and the performance of the proposed algorithms has been assessed through both experimental measurements and synthetic data. Numerical results have shown that the new approach to fault detection is comparable to conventional techniques based on spectral kurtosis, demodulation and spectral correlation, and it can outperform them in some cases; furthermore the simplicity of the proposed algorithms leads to an intrinsic robustness against the mechanical noise typical of practical scenarios.