现代制造工程Issue(11)：124-135,12.DOI:10.16731/j.cnki.1671-3133.2025.11.017
基于动态时空子图卷积网络的电机轴承故障声纹识别方法
Dynamic spatial-temporal subgraph convolutional network and its application in voiceprint-based asynchronous motor bearing fault diagnosis method
摘要
Abstract
;A Dynamic Spatial-Temporal Sub Graph Convolutional Network(DSTSGCN)was proposed to address limitations in ex-isting voiceprint-based fault diagnosis methods,including poor adaptability and insufficient dynamic graph construction capabilities caused by difficulties in capturing spatial-temporal coupling relationships.Firstly,a edge-level dynamic graph convo-lutional network was designed,where spatial correlations between voiceprint signals were adaptively learned through optimization of edge weights,and the sensitivity of traditional k-nearest neighbor graphs to hyperparameters was effectively mitigated.Secondly,dilated causal convolution was integrated with cross self-attention mechanisms,and a temporal feature fusion module was construc-ted to capture critical long-term dependency information within signals,thereby highlighting temporal relationships between signals.Finally,discriminative representations of fault features were enhanced through multi-signal spatial-temporal information fusion,enabling graph-level fault diagnosis.Experimental validation was conducted on a three-phase asynchronous motor test plat-form.Results demonstrated that the proposed DSTSGCN achieved 99.72％accuracy with only one training sample,outperforming seven comparative voiceprint-based diagnostic methods.关键词
电机轴承/声纹识别/故障诊断/图卷积网络Key words
motor bearing/voiceprint recognition/fault diagnosis/graph convolutional network分类
动力与电气工程引用本文复制引用
熊丽萍,吕辉,周建安..基于动态时空子图卷积网络的电机轴承故障声纹识别方法[J].现代制造工程,2025,(11):124-135,12.基金项目
河南省科技攻关项目(232102210171) （232102210171）
河南省高等学校重点科研计划项目(24B120002) （24B120002）
