残差胶囊网络在旋转机械故障诊断中的应用研究  

Research on Application of Residual Capsule Network in Fault Diagnosis of Rotating Machinery

作　　者：吴萍[1] 李曙生[1] WU Ping;LI Shusheng(Taizhou Polytechnic College,Taizhou 225316,China)

机构地区：[1]泰州职业技术学院,江苏泰州225316

出　　处：《机械制造与自动化》2024年第6期244-246,共3页Machine Building & Automation

摘　　要：针对旋转机械中的故障诊断需求,在传统的胶囊网络中引入残差块和模糊C均值聚类算法,构建残差胶囊网络故障诊断模型。在残差胶囊网络的基础上,引入注意力机制和G-K动态路由算法,构建注意力胶囊网络故障诊断模型。仿真分析表明:两种模型都能对故障进行精准测试,具有较强的表达能力和泛化能力。To meet the fault diagnosis requirements in rotating machinery,a residual capsule network fault diagnosis model is constructed by introducing residual blocks and fuzzy C-means clustering algorithm in traditional capsule networks.On the basis of residual capsule network,attention mechanism and G-K Dynamic routing algorithm are introduced to build a fault diagnosis model of attention capsule network.Simulation analysis shows that both models can accurately test faults and have strong expressive and generalization abilities.

关 键 词：旋转机械 故障诊断 胶囊网络 残差块 注意力机制 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]