Fault Diagnosis of Rotating Machinery Based on 1D–2D Joint Convolution Neural Network

Abstract

Convolutional neural network has been widely used in fault diagnosis of mechanical devices. In particular, two-dimensional convolutional neural network requires manual selection of multi-scale transformation to transform vibration signal into two-dimensional structure. Although one-dimensional convolution neural network can directly use the vibration signal for convolution processing, it can't make full use of the nonlinear information in one-dimensional space. In order to make full use of the advantages of one-dimensional and two-dimensional convolutional neural networks, this paper develops a one-dimension in tandem with two-dimension joint convolutional neural network (1D-2D JCNN) for rotating machinery fault diagnosis. More specifically, one-dimensional convolution is employed to adaptively obtain the multi-scale feature vectors of the vibration signal, and these feature vectors are constructed into two-dimensional maps, and then these two-dimensional vectors are used as the input of the two-dimensional convolutions neural network. Take the cross-entropy loss function as the loss function, and use the error back propagation algorithm to optimize the filter parameters of the 1D-2D JCNN model to obtain the final fault diagnosis model. Using the motor bearing data set and the worm gearbox data set, the experimental results show the excellent classification performance of bearings and gears under different working conditions. The average diagnostic accuracy of 10 runs on CWRU bearing data set is 99.92%, and the variance is 3.96e-6. The average diagnostic accuracy of 10 runs on worm gear data set is 99.82%, and the variance is 7.82e-6. Compared with the traditional fault diagnosis model and the latest convolution neural network method, the 1D-2D JCNN shows better diagnosis performance.