Rolling bearing vibration signal reconstruction based on joint indicators and fault diagnosis
In order to improve the effectiveness of feature extraction and accuracy of fault identifica-tion of rolling bearing,a signal reconstruction method based on joint indicators and a fault diagnosis method based on CWT-2DCNN were proposed.First,a joint indicator was constructed according to kurtosis and cross-correlation number to screen and reconstruct the intrinsic mode fuction(IMF)com-ponents obtained by ensemble empirical mode decomposition(EEMD).Secondly,continue wavelet transform(CWT)was used to extract the features of the reconstructed signal in time-frequency domain.Finally,a fault recognition model based on convolutional neural network(CNN)was constructed with time-frequency feature diagram as input,so as to realize the intelligent fault diagnosis of rolling bear-ing.The experimental results show that the fault diagnosis accuracy of the proposed signal reconstruc-tion and fault diagnosis method is 99.48%,and it still has a high correct recognition rate under strong noise,indicating that it has a strong generalization ability.
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