Aiming at the problems that vibration signal of bearing is easy to be interfered by noise and variable operat-ing conditions,and its feature information extracted by a single sensor is incomplete,a bearing fault diagnosis method based on complete ensemble empirical mode decomposition of adaptive noise(CEEMDAN),fast independent components analysis(FastICA)denoising and multiple-input convolutional neural networks(MCNN)is proposed.Firstly,the vibration signals collected by multiple sensing are divided into data sets separately and the CEEMDAN is used to obtain Inherent Modal func-tions(IMFs).Then,the IMFs with the kurtosis greater than 3 are selected to construct observation signal,the remaining IMFs are used to construct virtual noise signal.They are input to FastICA as two input sources to separate the feature vec-tors.Finally,MCNN is designed to identify fault types.The results show that the accuracy on the CWRU and XJTU-SY data sets is up to 99.94%and 99.64%.The accuracy is 96.95%and 98.29%in the anti-noise performance test with signal to noise ratio(SNR)of-8 dB.The accuracy is 99.00%and 99.23%in the anti-noise performance test with SNR of 0.The comparative experimental results show that the method can extract more comprehensive fault feature information and obtain higher accuracy of extraction.
维普
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