Fault characterization enhancement method for rolling bearings based on combination of weighted indicator screening and IMOMEDA

Abstract

As one of the core components of aeroengine, it is very important to find out the fault of intermediate bearing in time for the safety of aeroengine. Early bearing fault features are easily buried by background noise, which makes it difficult to extract fault features. To solve this problem, a fault feature enhancement method based on weighted index screening and IMOMEDA is proposed in this article. First, employ wavelet packet decomposition to analyze the vibration signal; and the kurtosis, correlation coefficient, and permutation entropy are fused into weighted indexes by entropy weight method to screen the node components with high signal-to-noise ratio for reconstruction; then, envelope autocorrelation analysis is used to optimize the deconvolution cycle parameter T of the multipoint optimal minimum entropy deconvolution adjusted; and then the optimized IMOMEDA is used to filter out the noise components of reconstructed signals to achieve the enhancement of the fault features; and finally, envelope demodulation is performed to identify the fault feature information. After the simulation signal verification, the signal peak factor processed by this method is increased from 7.3 to 9.7, so the fault characteristics are effectively enhanced. The data of different fault types measured by the aeroengine intermediate bearing fault simulation test bench are analyzed. The results show that the method can effectively filter out the strong background noise in the vibration signal of different fault types, enhance the weak fault characteristics related to the bearing fault, and can be used as one of the effective methods for the fault diagnosis of the intermediate bearing of the aeroengine.