A novel spectral coherence-based weighted envelope spectrum analysis method for bearing fault diagnosis

Abstract

Spectral coherence (SCoh) consists of spectral and cyclic frequencies and exhibits unique merits in simultaneously revealing the resonance frequency band and the fault characteristic frequency (FCF) of bearing signals. Most SCoh-based methods only consider the spectral frequency information, while the cyclic frequency information is ignored. However, the fault information and interference components are difficult to distinguish when only the spectral frequency is considered. To address this challenge, a novel bidirectional weighted enhanced envelope spectrum (BWEES) analysis method is proposed in this paper. First, an improved spectral weighting method is developed, which is conducted in the spectral frequency direction to enhance the resonance frequency band that carries the fault information. An autocorrelation function is exploited to reveal the cyclic information hidden in noises and appropriate weights are assigned to the spectral frequencies according to the magnitudes of autocorrelation values. Second, a cyclic weighting function is designed, which is operated in the cyclic frequency direction to enhance the FCF and suppress noise interference. The cyclic frequency components with the highest magnitudes are selected as a basis to reconstruct the one-dimensional cyclic frequency map for assigning different weights. Finally, the two-dimensional weighted bivariable map is constructed and then converted into spectral coherence to reveal the fault features. The BWEES is tested by simulation signals and experimental data, and compared with four state-of-art methods. In particular, the kurtosis values of BWEES in four different cases are 7.637, 12.831, 15.269, and 80.269, which are higher than other methods. The Gini index values of BWEES in four different cases are 0.866, 0.812, 0.424, and 0.306, which are also the largest. The above numerical results show that BWEES can achieve better performance.