计算机与数字工程2026，Vol.54Issue(6)：1584-1587,4.DOI:10.3969/j.issn.1672-9722.2026.06.008
基于轻量级深度学习的轴承故障诊断系统设计
Design of Bearing Fault Diagnosis System Based on Lightweight Deep Learning
摘要
Abstract
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.关键词
轴承/故障诊断/深度学习/嵌入式系统Key words
rolling bearing/fault diagnosis/deep learning/embedded system分类
机械制造引用本文复制引用
叶丹,姚凯学,何勇,先杰..基于轻量级深度学习的轴承故障诊断系统设计[J].计算机与数字工程,2026,54(6):1584-1587,4.基金项目
贵州省科技计划项目(编号:黔科合基础[2019]1130号,黔科合支撑[2020]2Y007号)资助. （编号:黔科合基础[2019]1130号,黔科合支撑[2020]2Y007号）
