计量学报2024，Vol.45Issue(6)：873-880,8.DOI:10.3969/j.issn.1000-1158.2024.06.13
基于边云协同的数控机床故障诊断联邦学习研究
Research on Federated Learning of Numerical Control Machine Fault Diagnosis Based on Edge Cloud Cooperation
摘要
Abstract
In order to solve the problem when the spindle bearing and cutter fault diagnosis model of NC machine tool is trained,it needs massive data and takes a long time.A federated mean learning fault diagnosis model based on edge-cloud collaborative architecture is proposed.Firstly,one-dimensional convolutional neural network model architecture is designed,and local model training is carried out on each edge client to reduce data upload scale and share computing pressure on cloud server side.Then,the model aggregation algorithm is optimized based on the accuracy on the cloud server side,and the edge client screening algorithm is improved to accelerate the convergence rate and improve the accuracy of the model.Thirdly,the KubeEdge cloud collaborative platform based on Kubernetes is built on the cloud server side to shorten the communication time of data transmission.Finally,the experimental results show that the accuracy of fault diagnosis in each edge client of the model is stable at about 87.5％.Compared with the control group,the convergence speed and training time are optimized.关键词
故障诊断/数控机床/边云协同/联邦学习/振动信号Key words
fault diagnosis/numerical control machine/edge cloud cooperation/federal learning/vibration signal分类
通用工业技术引用本文复制引用
徐玲艳,陆艺,赵静..基于边云协同的数控机床故障诊断联邦学习研究[J].计量学报,2024,45(6):873-880,8.基金项目
浙江省科技计划项目省级重点研发计划(2021 C01136) （2021 C01136）
