基于加权图卷积网络的多传感器旋转机械故障诊断OA

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.