A meta-path graph-based graph homogenization framework for machine fault diagnosis. (May 2023) - Yang, Chaoying; Liu, Jie; Zhou, Kaibo; Yuan, Xiaohui; Jiang, Xingxing
Abstract: Graph data-driven methods have swept the field of machine fault diagnosis by merits of modeling relationships between samples. Their performance is highly affected by the constructed graphs quality. Compared to the single-sensor data, multi-sensor data can provide more information, so as to construct higher-quality graphs. However, existing graph data-driven diagnosis methods using multiple sensors still have two limitations. Firstly, heterogeneous multi-sensor data are mainly processed as homogeneous data, ignoring the heterogeneity of heterogeneous multi-sensor data. Secondly, the heterogeneous graph is often with a complex graph structure, and consumes much computational cost to learn. To overcome these limitations, A meta-path graph-based graph homogenization framework for machine fault diagnosis is proposed. Heterogeneous multi-sensor data are converted into the heterogeneous graph, modeling the heterogeneity of heterogeneous multi-sensor data. Further, instead of directly inputting the heterogeneous graph into graph deep learning model, a heterogeneous graph homogenization framework is designed to generate a meta-path graph, reducing the complexity of graph structure and improving the graph quality. Finally, a graph convolutional network is used for graph feature learning, obtaining the diagnosis results. Verification experiments show that the proposed method performs better than machine learning-based and graph deep learning-based methods. In addition, discussive experiments show that the meta-path graph is with lower complexity in graph structure and a higher clustering accuracy than single-sensor data-based K-nearest neighborhood graph.