To diagnose the fault types of rolling bearings

thereby improving the safety of the equipment

an intelligent fault diagnosis method based on deep residual neural network was proposed. The multi-sensor fusion technology was used to improve the deep residual neural network

so that the recognition accuracy and robustness of the diagnosis model could be further improved. Firstly

through multi-sensor technology

the rich information of the equipment operating state was obtained

and then the primary features of the original vibration signal were extracted by short-time Fourier transform. Finally

the powerful learning ability of the deep residual network was used to further extract the advanced features from the primary features and identify the types of faults

thus achieving the rolling bearing fault diagnosis. The experimental data of rolling bearing were used to verify the effectiveness of the proposed method

and deep convolution neural network-based method and single sensor-based method were taken as contrast methods to test the same dataset. Results show that the proposed method could not only accurately identify faults

but also had good generalization ability and anti-noise ability. The fault diagnosis accuracy reached 100%

and when single or multiple sensors were affected by strong noise

the diagnostic accuracy was at least 93.78% and 82.54% respectively.