Bearing Fault Diagnosis Across Working Conditions Based on GADF and RDSAN
In order to solve the problem that the characteristic distribution of vibration data obtained under different working conditions is different and the fault characteristics are not obvious due to the existence of noise,a cross-working bearing fault diagnosis method based on qualified ram angle difference field(GADF)and residual depth sub-domain adaptive(RDSAN)model is proposed.Firstly,in order to make full use of the advantages of GADF in the differential display of fault characteristics,the image data set cor-responding to the time domain signal of one-dimensional vibration of rolling bearings is generated by GADF.Then,the data set is fed into the RDSAN model,where the common features of source domain and target domain are further extracted using the ResNet-18 network structure pre-trained by the improved im-age set,and the local maximum mean difference(LMMD)is introduced to calculate the matching condi-tion distribution distance for subdomain adaptation.Finally,cross-working test is carried out on CWRU roll-ing bearing data set with 0.5 dB Gaussian white noise.The results show that the average diagnostic accura-cy of the proposed method reaches 96.8%.The proposed method is compared with other diagnostic meth-ods,and the results prove the effectiveness and superiority of the proposed method.
rolling bearingfault diagnosisvariable operating conditionsgram angle difference fieldsub domain adaptation
NETL
NSTL
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