控制理论与应用2026，Vol.43Issue(3)：521-529,9.DOI:10.7641/CTA.2024.30829
基于加权图卷积网络的多传感器旋转机械故障诊断
Multi-sensor rotating machinery fault diagnosis using weighted graph convolutional network
摘要
Abstract
Multi-sensor data can provide more comprehensive and accurate information for fault diagnosis,but current deep learning algorithms modeled in Euclidean space have difficulty effectively handling the complex interactions and spa-tial relationships between sensors.Additionally,the non-stationary characteristics of vibration signals in rotating machinery greatly affect the effectiveness of fault diagnosis.To address these issues,this paper proposes a novel multi-sensor fault diagnosis method for rotating machinery based on a weighted graph convolutional network.The Hilbert-Huang transform(HHT)is used to adaptively extract fault features,overcoming the impact of signal non-stationarity.Considering the strong expressive power of graph structures in spatial relationships and the powerful feature learning capabilities of graph convo-lutional networks,a weighted HHT graph is constructed based on the distance metric between sensor node feature vectors,and a two-layer graph convolutional network is built for fault diagnosis.Additionally,two regularization terms are intro-duced into the network's loss function to improve diagnostic accuracy.Experimental results on public datasets verify the effectiveness and superiority of the proposed method compared to other approaches.关键词
多传感器/故障诊断/希尔伯特-黄变换/图卷积网络Key words
multiple sensors/fault diagnosis/Hilbert-Huang transform/graph convolutional network引用本文复制引用
胡艳艳,衣骁捷,彭开香..基于加权图卷积网络的多传感器旋转机械故障诊断[J].控制理论与应用,2026,43(3):521-529,9.基金项目
国家自然科学基金项目(62273038,U21A20483),智控实验室开放基金项目(ZKSYS-KF03-05)资助.Supported by the National Natural Science Foundation of China(62273038,U21A20483)and the Open Fund of Intelligent Control Laboratory(ZKSYS-KF03-05). （62273038,U21A20483）
