A Semi-Supervised Matrixized Graph Embedding Machine for Roller Bearing Fault Diagnosis Under Few-Labeled Samples

Exploring historical measurement data-driven health monitoring schemes for roller bearings is a current research hotspot. In engineering practice, the type of fault data obtained is often unknown and requires expensive costs to be annotated. However, most current intelligent diagnostic methods are based on the assumption that the labeled fault data is sufficient, so as to effectively establish the nonlinear mapping relationship between monitoring signals and health status. For this issue, a newly intelligent diagnosis method based on semi-supervised matrixized graph embedding machine (SMGEM) is proposed. In SMGEM, the geometric similarity relationship of unlabeled and labeled samples is obtained, which is subsequently embedded by incorporating a manifold regularization into the SMGEM model, so that SMGEM can use the structure information of unlabeled samples to assist modeling. Meanwhile, a weighted nuclear norm is used to highlight the importance of large singular values, so that a more accurate weight matrix can be constructed. The proposed method is verified by several roller bearing fault datasets, and experimental results demonstrate that the proposed semi-supervised diagnosis method can use a few labeled samples to obtain a better identification accuracy.