Abstract: Due to the poor working environment of the bearing, its faults frequently occur, and it was difficult to quickly diagnose and locate the bearing fault. For this reason, an intelligent diagnosis method for bearing faults based on comprehensive information fusion neural network was proposed. Firstly, the working principle of preneural network was introduced, the link weight coefficient training method of pre-neural network was deduced, and the algorithm flow of preneural network was formulated. Based on D-S evidence theory and Dempster combination rule, a fault diagnosis method of post neural network was designed. Then, a bearing fault diagnosis method based on comprehensive information fusion neural network was proposed, so that the advantages of the two neural networks were fused. Finally, the experimental verification was carried out based on the bearing experimental data of Case Western Reserve University. The research results show that the average fault recognition rate without post fusion module is 90.45%, the average fault recognition rate without prefusion module is 89.93%, and the average fault recognition rate of comprehensive information fusion neural network is 99.33%. The above data show that the integrated information fusion neural network has the highest fault recognition accuracy and strong robustness, which verifies the effectiveness and accuracy of comprehensive information fusion neural network in bearing fault diagnosis.

Key words:  bearing fault;intelligent diagnosis; pre-neural network algorithm; integrated information fusion; fault recognition rate;robustness