Multisensor Fusion on Hypergraph for Fault Diagnosis

• Published in: IEEE transactions on industrial informatics 2024-08, Vol.20 (8), p.10008-10018
• Main Authors: Yan, Xunshi, Shi, Zhengang, Sun, Zhe, Zhang, Chen-An
• Format: Article
• Language: English
• Subjects: Algorithms; Convolution; Correlation; Data integration; Fault diagnosis; Feature extraction; graph neural network (GNN); Graph theory; hypergraph neural network (HGNN); information fusion; Machine learning; Multisensor fusion; Neural networks; Sensor fusion; Sensor phenomena and characterization
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3393137

Multisensor information fusion techniques based on deep learning are crucial for machinery fault diagnosis. However, there are two major issues in previous research. First, the relationship between multisensor samples is disregarded, which is important to enhance the diagnostic performance. Second, the structure of the fusion algorithm becomes extremely complex with prolonged training when dealing with machinery equipped with a large number of sensors. To address the aforementioned two issues, our study proposes a new multisensor fusion mechanism that fuses multisensor information on hypergraphs, by building a single-sensor fusion hypergraph and a multisensor fusion hypergraph in the sensor space to embed the fault samples as nodes. In addition, a dual-branch hypergraph neural network is designed to compute the two hypergraphs to obtain the feature representation of the samples and diagnose faults. The algorithm is validated on two datasets for its performance.