A time–frequency-based maximum correlated kurtosis deconvolution approach for detecting bearing faults under variable speed conditions

Abstract

Rolling bearing vibration signals induced by local faults are highly correlated with structural dynamics. This leads to a great deal of research about signal processing for rolling bearing condition monitoring and fault detection. However, the common diagnostic approaches, which were proposed to manage vibration signals with constant speeds, are unavailable for variable speed cases. Tacholess order tracking is a powerful method to break the limitations of conventional methods while avoiding trouble with tachometer installation and reducing measurement costs. Time–frequency analysis methods are used to estimate the instantaneous rotation frequency (IRF) from vibration signals directly. However, it is difficult to extract the IRF accurately due to the strong nonstationary properties of the signal. Therefore, parameterized time–frequency transform (PTFT) methods are proposed to solve this problem. Polynomial chirplet transform (PCT) is one of the PTFTs that can produce an excellent time–frequency representation with a polynomial kernel function. In this paper, the PCT is employed to estimate the IRF of rolling bearings from the vibration signals. On this basis, a maximum correlated kurtosis deconvolution-based envelope order spectrum is applied to detect the bearing fault characteristic order. The efficiency of the proposed method is certified by numerical signal and rolling bearing vibration data. The diagnostic results indicate that the new fault detection algorithm is superior for rolling bearing fault diagnosis under varying speed conditions.