Rolling bearing fault diagnosisusing deep neural network

Abstract:

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.