A deep convolutional neural network model with two-stream feature fusion and cross-load adaptive characteristics for fault diagnosis

Abstract

Abstract Research aimed at diagnosing rolling bearing faults is of great significance to the health management of equipment. In order to solve the problem that rolling bearings are faced with variable operating conditions and the fault features collected are single in actual operation, a new lightweight deep convolution neural network model called FC-CLDCNN, composed of a convolution pooling dropout group with two-stream feature fusion and cross-load adaptive characteristics, is proposed for rolling bearing fault diagnosis. First, the original vibration signal is transformed into a one-dimensional frequency domain signal and a two-dimensional time-frequency graph by fast Fourier transform and continuous wavelet transform. Then, the one-dimensional frequency domain signal and two-dimensional time-frequency diagram are input into the two channels of the model respectively to extract and recognize the one-dimensional and two-dimensional features. Finally, the one-dimensional and two-dimensional features are combined in the fusion layer, and the fault types are classified in the softmax layer. FC-CLDCNN has the characteristics of two-stream feature fusion, which can give full consideration to the characteristics of rolling bearing fault data, so as to achieve efficient and accurate identification. The Case Western Reserve University (CWRU) dataset is used for training and testing, and it is proved that the proposed model has high classification accuracy and excellent adaptability across loads. The Machinery Failure Prevention Technology (MFPT) dataset was used to validate the excellent diagnostic performance and generalization of the proposed model.