Research on bearing fault diagnosis of reciprocating compressors based on GMDE and MFO-MKELM algorithms
LI Yanyang
WANG Jindong
NING Liuyang
MA Lei
Abstract：[Objective]Aiming at the problem that the bearing fault feature extraction is difficult and the recognition accura-cy is not high due to the characteristics of local strong non-stationarity and nonlinearity of the vibration signal of reciprocating compressor bearing clearance,a new method based on GMDE and MFO-MKELM algorithm was proposed.[Methods]First,spread on multiscale entropy in the process of coarse graining,the average coarse graining way to a certain extent,"neutralize"the dynamics of the original signal mutation behavior,and reduce the accuracy of the entropy analysis.A generalized multiscale entropy algorithm spread,application of the reciprocating compressor vibration signals of bearing clearance for fault feature ex-traction was proposed;then,the polynomial kernel function and the improved Gaussian kernel function were linearly combined to construct the multiple kernel extreme learning machine intelligent recognition algorithm,and the fault diagnosis research was carried out on the extracted feature vector set.[Results]Simulation results show that the recognition accuracy of the fault diagno-sis method is as high as 98.6%,which effectively realizes the intelligent diagnosis of different types of bearing faults.
Keywords：Reciprocating compressorGeneralized multi-scale dispersion entropyMoth-flame capture algorithmMulti-kernel extreme learning machine
Publication Date：2025-02-14
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：7( 170-176 )