Dimensionality reduction of rolling bearing fault data based on graph-embedded semi-supervised deep auto-encoders

Conventional shallow models struggle to extract effective features from high-dimensional and sparse bearing fault data, which negatively impacts fault classification accuracy. To address this issue, we propose a dimensionality reduction method for rolling bearing fault datasets based on a Graph Embedding Semi-supervised Deep Auto-Encoder (GESDAE). This method constructs traditional fault datasets into a data network graph embedding structure and inputs them into a sparse dropout regularized semi-supervised deep auto-encoder for dimensionality reduction. The supervised component utilizes first-order proximity to maintain the local network graph embedding structure, while the unsupervised component employs second-order proximity to capture the global network graph embedding structure. By reasonably introducing a sparse dropout regularization term in the original objective function, the method helps prevent model overfitting and enhances the robustness and generalization ability of GESDAE. The effectiveness of the proposed method is validated using vibration signals from rolling bearing systems on two different experimental platforms. Results show that the proposed method achieves a correct recognition rate of 97.3 % for rolling bearing faults, with a 4.8 % improvement compared to state-of-the-art dimensionality reduction methods. Furthermore, visualization and convergence analysis demonstrate that the proposed method not only retains highly nonlinear local and global network graph embedding structures but also significantly enhances the separability of fault features, showcasing its superior robustness and generalization performance in fault diagnosis systems.