Fault diagnosis of rolling bearings based on WTMSST and adaptive parameter VMD
To address the common problems in the conventional time-frequency analysis methods used in current bearing fault diagnosis, such as relatively discrete transform coefficient distribution on the time-frequency plane and blurry energy in the time-frequency spectrum, this paper proposes a rolling bearing fault diagnosis method based on wavelet transform modulated synchronous squeezing transform ( WTMSST) in conjunction with variational mode decomposition ( VMD) optimized by dung beetle optimizer ( DBO) .First, the method adopts a WTMSST algorithm optimized by the weighted time-synchronous squeezing transform ( WTSST ) , reducing the group delay under strong frequency changes through fixed-point iteration.Then, by employing the smallest envelope entropy as the fitness function, the DBO algorithm optimizes the input parameters of VMD.Following the reconstruction of the signal based on kurtosis, the WTMSST time-frequency analysis method is employed for fault feature extraction.Experiments are conducted using the Case Western Reserve University data set.Tests are conducted using the data set of Case Western Reserve University.Our results show the method accurately describes the impact characteristics of the signal and performs better than the previous processing methods.
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