Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine
FU Qiang
HU Dong
YANG Tongliang
LUO Guoqing
TAN Weimin
Abstract：In response to challenges such as large sampling data,extended diagnosis time,and subjective fault feature selection in traditional bearing fault diagnosis,based on compressed sensing(CS)and deep multi-kernel extreme learning machine(D-MKELM)theory,a CS-DMKELM intelligent diagnosis model for rolling bearings was proposed.Firstly,sparse signals were obtained through threshold processing of transformed domain signals.A Gaussian random matrix was employed as the measurement matrix to compress the processed data.Secongly,the compressed data was used as the input signal for the D-MKELM.Particle swarm optimization(PSO)algorithm was applied to optimize critical parameters,enabling intelligent fault diagnosis.Results demonstrate that the proposed method,using only a small amount of bearing diagnostic data,automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM.The proposed method enables rapid fault diagnosis of bearings.With a diagnostic time of 0.55 s,a final recognition accuracy of 99.29%was achieved.The proposed method reduces the diagnostic time and exhibits the high diagnostic accuracy,providing a new approach for handling massive bearing data in the fault diagnosis.
Keywords：Compressed sensingBearingKernel functionExtreme learning machineFault 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：9( 48-56 )