现代电子技术2026，Vol.49Issue(6)：184-188,193,6.DOI:10.16652/j.issn.1004-373x.2026.06.027
多模态数据驱动的智能故障诊断方法
Method of multi-modal data-driven intelligent fault diagnosis
摘要
Abstract
The multi-modal data can provide more comprehensive and multi-dimensional operation status information of mechanical equipment than the single-modal data in the data-driven intelligent rotating machinery fault diagnosis(RMFD).The method of multi-modal data-driven intelligent fault diagnosis can significantly improve the accuracy and robustness of RMFD.However,the multi-modal data collected by different types of sensors in the operation of rotating machinery equipment are large scale and have significant heterogeneity and complementarities.How to effectively extract and fuse the fault features of different modalities is a key problem to be solved in multi-modal data-driven fault diagnosis.On this basis,a method of multi-modal data-driven intelligent fault diagnosis is proposed.The multimodal data consisting of vibration signals and current signals are constructed into multiple multimodal radius graphs containing multimodal fault features based on the radius neighbor algorithm,so that the model can effectively learn and extract deep-level information of multimodal fault features.The input and output of each layer in the graph sample and aggregate(GraphSAGE)network are weighted and fused to fully capture the potential associa-tions in multi-modal data and improve the expression ability of the model.A series of experiments are carried out to verify the effectiveness of the proposed method,and the results show that the method has high accuracy in fault diagnosis.关键词
多模态/滚动轴承/故障诊断/加权融合/GraphSAGE网络/数据驱动Key words
multi-modal/rolling bearing/fault diagnosis/weighted fusion/GraphSAGE network/data driven分类
信息技术与安全科学引用本文复制引用
鲍逸国,万烂军,倪炜..多模态数据驱动的智能故障诊断方法[J].现代电子技术,2026,49(6):184-188,193,6.基金项目
湖南省教育厅重点项目(24A0391) （24A0391）
湖南省自然科学基金面上项目(2026JJ50235) （2026JJ50235）
湖南省自然科学基金区域联合项目(2025JJ70030) （2025JJ70030）
