Fault diagnosis method of rolling bearing based on MTF and DenseNet

The fault diagnosis methods of rolling bearing based on model and signal processing and analysis have the problems of modeling difficulty and signal characteristic extraction difficulty. The rolling bearing fault diagnosis method based on shallow machine learning has limited capability to learn the characteristics of complex data. The convolutional neural networks are often used in rolling bearing fault diagnosis methods based on deep learning. But with the deepening of the network, gradient dispersion or disappearance will occur. And directly converting the rolling bearing vibration signal into one-dimensional or two-dimensional images as network input will not preserve the time correlation between the signals, resulting in the loss of signal information. To solve these problems, a fault diagnosis method for rolling bearing based on Markov transition field(MTF) and densely connected convolutional networks(DenseNet) is proposed. The vibration signal of the rolling bearing is coded by MTF to generate a two-dimensional image. The time sequence information and the state transition information of the signal are preserved. The two-dimensional image is taken as the input of DenseNet, and the fault characteristics of the rolling bearing vibration signal are extracted through DenseNet. The method enhances the propagation of characteristic information, makes full use of characteristic information, and then realizes fault classification and identification. The data on the Case Western Reserve University bearing dataset is used for the test. The results show that the method can effectively identify the fault types of rolling bearings, and the accuracy of fault diagnosis is 99.5%. In order to further verify the fault diagnosis capability and superiority of this method when the motor load changes, four kinds of network input images, namely, gray-scale image, envelope spectrum image, cepstrum image and MTF generation image, are selected for comparative experiments with the method of combining three networks, namely, Inception, ResNet and DenseNet. The results show that the fault diagnosis accuracy of different methods is higher when the motor load is unchanged than when the motor load is changed. The fault diagnosis accuracy of MTF+DenseNet method is higher than that of other methods. The proposed method still has a high fault diagnosis accuracy when the motor load changes, with an average value of 94.53% and good generalization performance.