噪声与振动控制2024，Vol.44Issue(2)：156-163,8.DOI:10.3969/j.issn.1006-1355.2024.02.025
卷积神经网络与知识图谱结合的轴承故障诊断
Bearing Fault Diagnosis Based on Convolution Neural Network and Knowledge Graph
李志博 1李媛媛 1蔡寅1
作者信息
- 1. 上海工程技术大学 电子电气工程学院,上海 201620
- 折叠
摘要
Abstract
Aiming at the problems of using vibration data only and the fuzzy diagnosis results in the current fault diagnosis of rotating machinery,a fault diagnosis method based on convolution neural network(CNN)and knowledge graph is proposed.In this method,the original bearing data and mechanism knowledge are taken as the input,and the entity extraction and data annotation are carried out.The proposed end-to-end multi-scale attention mechanism neural network model is used to carry out the fault diagnosis of bearings,realize relationship extraction,and finally build a knowledge map to realize the detailed display of fault information for auxiliary diagnosis.Experimental verification is carried out on two datasets,and a new data processing method is adopted.The results show that the value of weighted F1 in the proposed algorithm is 11.03%higher than that of the benchmark model in 160 fault types,and the traditional fault diagnosis experiments and other comparative algorithms fully prove that the proposed model has strong stability and generalization performance.关键词
故障诊断/卷积神经网络/知识图谱/轴承Key words
fault diagnosis/convolutional neural network/knowledge graph/bearing分类
信息技术与安全科学引用本文复制引用
李志博,李媛媛,蔡寅..卷积神经网络与知识图谱结合的轴承故障诊断[J].噪声与振动控制,2024,44(2):156-163,8.
