A new difference feature extraction method of slewing bearings in wind turbines via optimization bispectrum domain model

Abstract

The slewing bearing is a critical component in large equipment like shield machines and wind turbines. Because slewing bearings operate in complex situations with fluctuating speed and load on a regular basis, the vibration signal they produce contains several interferences, making fault features difficult to identify. The specific objective of this study is to provide a new fault diagnosis method, named difference optimization bispectrum, for slewing bearing signals under strong noise interference. The method designs a convex optimization bispectrum model by the convex optimization theory, covering the shortage of traditional decomposition by differentiating features. Based on the model, a two-dimensional weight coefficient is constructed to calculate the difference optimization bispectrum, which reduces the noise and enhances the features in positive and negative bispectrum-domain. This study offers a fresh perspective on extraction of fault information from the signal under strong noise interference, making an original contribution for the fault diagnosis of the slewing bearing. The experiment work presented here provides the practical effect of the method for the slewing bearing signals.