A Novel Method for Predicting Fault Labels of Roller Bearing by Generalized Laplacian Matrix

Because mechanical failures are accompanied by contingency and randomness, fault data is often difficult to obtain, and fault labels are also difficult to assign. The lack of data and fault labels have become important issues that restrict the development of fault diagnosis. The paper proposed a generalized Laplacian label prediction (GLLP) algorithm, which mainly uses the generalized Laplacian matrix and calculated a new locally smooth term. Therefore, data points with ambiguous and unclear labels will be assigned a small label value, while samples with more certain labels can get a more confident label value. The effectiveness of the method is verified on the public dataset and the real test rig dataset, and it is expected that this method can be extended to more complex mechanical system fault diagnosis.

Automated summary

The proposed GLLP algorithm utilizes the generalized Laplacian matrix and a new locally smooth term to address the issue of insufficient data and labels in mechanical fault diagnosis. The effectiveness of the method is demonstrated through validation on various datasets.