基于轻量级深度学习的轴承故障诊断系统设计OA

Aiming at the problems that the current bearing fault diagnosis model based on deep learning has many parameters,complex models and is difficult to apply to the actual industrial scene,a portable embedded controller and lightweight deep learning diagnosis model,which is easy to deploy in the industrial scene,is proposed for real-time fault diagnosis of motor bearing operation status.After experimental verification,the accuracy of the proposed embedded diagnosis system reaches 98.1%,and the average speed is 126ms/time.Its hardware equipment is small in size,low in cost,good in real-time,easy to maintain and manage,easy to install and promote to industrial production line environment.