Gearbox fault diagnosis based on improved northern goshawk algorithm and hybrid core extreme learning machine
DU Dong-sheng
WANG Meng-jiao
MAO Ze-hui
ZHAO Huan-yu
Abstract：Aiming at the problem of planetary gearbox fault diagnosis,this paper proposes a fault diagnosis method of planetary gearbox based on the improved northern northern goshawk algorithm(INGO)and hybrid core extreme learning machine(HKELM).Firstly,savitzky-golay(SG)filtering is introduced to denoise the original signal of the gearbox.In addition,the time varying filtering empirical mode decomposition(TVF-EMD)is used to decompose the denoised signal into multiple intrinsic mode functions(IMF).And the variance contribution rate,correlation coefficient and information entropy are used to screen out the optimal IMFs.After the optimal IMFs are reconstructed,the reconstructed signal is denoised by time synchronization average(TSA)to reduce the data calculation amount of the fault diagnosis model.Secondly,INGO algorithm is obtained by applying Tent chaotic mapping,hybrid sine cosine algorithm and Levy flight strategy to improve the NGO algorithm.At the same time,the cosine factor is introduced to balance the global and local development capabilities of sine cosine algorithm.Finally,the INGO algorithm is used to optimize HKELM to improve the fault diagnosis accuracy of HKELM model.The proposed scheme is applied to two different public data sets to test the model,and the experimental results show that the proposed method is feasible and advantageous.
Keywords：hybrid kernel extreme learning machineimproved northern goshawk algorithmtime varying filter based empirical mode decompositionfault diagnosis
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 796-804 )