Rolling Bearing Fault Diagnosis Technology Based on Deep Learning

With the systematization, complexity, and intelligence attributes of modern machinery, a large number of rolling bearings are used in various mechanical equipment. As the core component of the mechanical equipment, the health state of the rolling bearing is of vital importance as its failure can affect the normal operation of the equipment and threaten the safety of people's lives and property. At the same time, with various current testing equipment used in machine operation and maintenance, a large amount of data has been collected and used to predict the health status of the bearings. In this context, we analyzed the rolling bearing fault mechanism and its diagnosis techniques, then proposed the method to do the rolling bearing fault diagnosis by using deep learning. The bearing fault diagnosis model is proposed based on deep learning. The model contributes to the sustainable use of rolling bearings and machinery accident prevention.