In order to ensure the safe operation of industrial machinery and equipment, avoid serious equipment damage caused by bearing damage, and realize the fault diagnosis system of rolling bearings under different working conditions on mechanical equipment, a fault diagnosis system of rolling bearings under different working conditions was designed based on convolutional neural network. The system gram matrix method is used to convert one dimensional time series data for the characteristics of two-dimensional figure, convolution neural network training to maximize learning characteristic information in the data, the trained model deployed in PC written in LabVIEW real-time fault diagnosis, the method of case western reserve university in the United States bearing experiment data center data sets, experimental verification: On the bearing data set of Case Western Reserve University in the United States, the identification accuracy of the method is 99.85% and the comprehensive identification accuracy is 91.67% under different operating conditions. Experimental results show that the method achieves relatively accurate identification effect and has good generalization ability. It has accumulated application experience for fault diagnosis of rolling bearing under variable working conditions.