Fault diagnosis of roller bearings based on Laplacian energy feature extraction of path graphs

• The path graph is first introduced into the time series analysis in a manifold perspective.
• Laplacian energy (LE) is developed to measure the fault feature of rolling bearings from graph spectrum domain.
• A fault diagnosis method based on LE and Mahalanobis distance (MD) is proposed.

Feature extraction of roller bearing is always an intractable problem and has attracted considerable attention for a long time. The vibration signal of roller bearing can be treated as the path graph in a manifold perspective. Generally, vibration signals of roller bearings with different faults have different correlation matrices of path graphs which including different adjacency matrices and Laplacian matrices. Therefore, as a complexity feature of the path graph, the Laplacian energy (LE) can be employed to analyze the roller bearing vibration signals. In this paper, LE is introduced as the fault feature of bearing vibration signals from graph spectrum domain and then a new fault diagnosis method based on the LE single feature extraction and Mahalanobis distance (MD) criterion function is proposed and applied to the analysis of roller bearing vibration signals. Experimental analysis results show that the proposed method can identify the roller bearing faults accurately and effectively only with a small amount of sampling points and training samples.