Early Wear Diagnosis of Gears Based on Spectrum Correlation Analysis

doi:10.12068/j.issn.1005-3026.2023.01.003

Abstract

Abstract: Gear fault detection based on vibration analysis has been proven to be effective in fault identification. However， the extraction and identification of vibration signals characterizing early wear have not been solved well. This paper proposes a method for early wear diagnosis of gears， combining variational mode decomposition (VMD) based on spectrum correlation analysis with kernel support vector machine (SVM). For weak gear vibration signals that can characterize early wear， the modal numbers are initialized by an approximate complete reconstruction criterion. Meanwhile， the frequency corresponding to the maximum value of the signal power spectral density is used to initialize the center frequency of the VMD method. It is used to effectively extract gear wear information from gear vibration signals and then be combined with kernel support vector machines for early wear diagnosis of gears. The experimental results show that the proposed method can effectively overcome the problem that the modal numbers cannot be preset with large background noise， be with better robustness to noisy situations， and achieve a diagnostic accuracy of 94.4%， which provides a solution for early wear detection of gears.

Key words: extraction of gear vibration signals； early wear diagnosis； spectrum correlation analysis； variational mode decomposition (VMD); support vector machines (SVM)

CLC Number:

TP273
TH132

WANG Hong-min， CHAN Liang. Early Wear Diagnosis of Gears Based on Spectrum Correlation Analysis[J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 18-25.

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