Traditional intelligent diagnosis method relying much on expert knowledge and manual extraction data features takes a lot of work. Based on the advantages of deep learning in feature extraction and processing of big data

a method of rolling bearing fault diagnosis based on convolution neural network and kurtosis was studied. This method was used to analyse four kinds of vibration signal of the normal state

the inner race fault

the outer race fault and the ball fault. The vibration signal was processed in segments to obtain kurtosis

which was converted into gray images by data-to-image method. Finally

these were fed into convolution neural network model to complete rolling bearing fault classification. In the case of rolling bearing fault diagnosis

the improved model had a diagnostic accuracy of 99.5%

which was higher than 95.8% of the traditional support vector machine (SVM) algorithm.