| Popis: |
Bearings play an important role in current industrial production. The fault of bearings can cause economic losses and even casualties. Therefore, it is very important for industrial production to be able to diagnose bearing faults. This paper proposes a bearing fault detection method based on convolutional neural network: the original bearing vibration data collected by the sensor is input into the convolutional neural network, and the calculation of the convolutional neural network can obtain the fault type of the bearing. In this paper, after building the model, the number of neurons in the fully connected layer is optimized and calculated on the data set of the rolling bearing data center of the Western Reserve University. The accuracy rate is as high as 99.7%. |
