A feature extraction method based on improved resonance sparse decomposition for early faults in rolling bearings
SUN Meng
GAO Bingpeng
CHENG Jing
Abstract：To overcome the difficulty in early fault diagnosis with weak fault characteristics of rolling bearings that are easily drowned out by noise in the complex operation environment,an early fault diagnosis method was proposed by integrating the improved artificial gorilla troops optimizer(IGTO)algorithm,the optimized resonance-based sparse signal decomposition(RSSD),multi-parameter and sparse maximum harmonics-to-noise-ratio deconvolution(SMHD)method.Firstly,taking the squared envelope spectrum correlated kurtosis(SE-SCK)negative value of the low resonance component as the objective function,IGTO was used to simultaneously optimize the quality factor Q,weight coefficient λ and Lagrange multiplier μ of RSSD,for the achievement of the optimal matching of wavelet basis function and dissipation function.Secondly,the obtained optimal low resonance component was inputed into SMHD for filtering processing.Finally,the fault features were extracted by the perform envelope spectrum analysis.The algorithm comparison experiments show that the proposed IGTO algorithm has significantly improved optimization performance.The results of simulation and XJTU-SY bearing full life cycle fault signal test show that the proposed method is more useful in extracting early weak fault characteristics of bearings.
Keywords：Improved artificial gorilla troops algorithmResonance sparse decompositionSquare envelope spectrum correlation kurtosisSparse maximum harmonics-to-noise-ratio deconvolutionEarly fault diagnosis
Publication Date：2025-06-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 17-26 )