基于边云协同的数控机床故障诊断联邦学习研究OA北大核心CSTPCD
Research on Federated Learning of Numerical Control Machine Fault Diagnosis Based on Edge Cloud Cooperation
为解决数控机床主轴轴承和刀具在进行故障诊断模型训练时需要大量数据且耗时长的问题,提出一种基于边云协同架构的联邦平均学习故障诊断模型.首先,设计一维卷积神经网络模型架构,在各边缘客户端进行本地模型训练,以减小数据上传规模和分担云服务器端计算压力;其次,在云服务器端基于准确率优化模型聚合算法,改进边缘客户端筛选算法,以加快模型收敛速度,提高模型准确率;再次,在云服务器端搭建基于Kubernetes的KubeEdge边云协同平台,以缩短数据传输的通信时…查看全部>>
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 p…查看全部>>
徐玲艳;陆艺;赵静
中国计量大学计量测试工程学院,浙江杭州 310018中国计量大学计量测试工程学院,浙江杭州 310018杭州沃镭智能科技股份有限公司,浙江杭州 310018
故障诊断数控机床边云协同联邦学习振动信号
fault diagnosisnumerical control machineedge cloud cooperationfederal learningvibration signal
《计量学报》 2024 (6)
873-880,8
浙江省科技计划项目省级重点研发计划(2021 C01136)
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