Rolling Bearing Fault Diagnosis Based on Improved VMD Binary Image
Author:佚名 Author Unit: 1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; 2 .Hunan Sine Electronic Technology Company Limited, Xiangtan 411100, China.
Abstract：Because it is easy to feature signal loss during signal feature extraction by the Variational Mode Decomposition (VMD), we have proposed a method of rolling bearing fault diagnosis based on the improved VMD algorithm of binary diagram and then combined with the LeNet-5 network. It can convert the vibration signal into a binary image with distinct characteristics, and the LeNet-5 network can identify the fault. Experimental results show that the recognition accuracy of this proposed model can achieve 100％, which exceeds 97.5％ of the traditional support vector machine (SVM) classiffer and 97.61％ of empirical mode decomposition (EMD) binarized graph +
convolutional neural network (CNN). In the anti-noise ability test, the recognition accuracy of the model is still as high as 100％, with good noise immunity and stability.
