Gearbox fault diagnosis method with multi-domain feature fusion based on attention mechanism
CHEN Fuxing
LENG Sheng
GAO Hailin
HUANG Haize
LU Fengxia
TANG Peng
Abstract：[Objective]Aiming at the limitation that single data domain models are difficult to accurately identify subtle fault features in gearbox fault diagnosis,a fault diagnosis method with multi-domain feature fusion under attention mechanism was proposed to improve diagnostic accuracy,stability and generalization ability.[Methods]Firstly,dimensionless features and spectral features were extracted from the time domain and frequency domain of vibration signals.Secondly,deep time-frequency domain features were extracted by combining continuous wavelet transform with convolutional neural network(CNN).Then,an attention mechanism was introduced to dynamically weight and fuse multi-domain features,strengthening key features and weakening redundant information.Finally,a classifier was used to complete fault identification,and the effectiveness of the method was verified based on a secondary gearbox test dataset.[Results]Test results on a secondary gearbox test dataset show that the proposed method achieves a diagnostic accuracy of 99.77%,outperforming single-domain models and verifying its effectiveness and stability in identifying weak faults and adapting to multiple operating conditions.
Keywords：Gearbox fault diagnosisFeature extractionAttention mechanismMulti-domain feature fusion
Publication Date：2026-02-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 12-20 )