A Fast Graph Construction-Driven Rotating Machine Fault Diagnosis Method Using Edge Predictor

Graph-based machine fault diagnosis methods are successfully used in extracting relationship information. However, the heavy computational burden of K-nearest neighbor graph (KNNG) has limited its application. To overcome it, a fast graph construction-driven rotating machine fault diagnosis method using an edge predictor is proposed in this article. The edge predictor, pretrained on an edge connection prediction task, is designed to learn how to get a distance matrix from an initial KNNG (IKNNG). Subsequently, numerous samples are directly input to the edge predictor, obtaining the generated distance matrix and enabling fast KNNG construction. Compared to the traditional KNNG construction method, this approach outputs directly without calculating the distance matrix, significantly reducing the computational burden. The experimental results show that the performance of the proposed method is as well as existing graph data-driven methods. Furthermore, theoretical analysis reveals that the quality of the constructed KNNG is similar to the KNNG obtained by traditional distance matrix calculations, but with a significantly reduced computational load.