Rolling bearing residual life prediction method based on improved graph convolutional network and gating cycle unit
By combining an improved graph convolutional network with a gated recurrent unit, the problem of factor interference in the prediction of the remaining life of rolling bearings is solved, and high-precision and robust prediction results are achieved.
Patent Information
- Application Number
- CN202410740779.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-07
- Publication Date
- 2026-04-21
AI Technical Summary
In complex industrial environments, existing data-driven methods for predicting the remaining life of rolling bearings are subject to interference from external and internal factors, resulting in poor prediction accuracy.
An improved combined model of graph convolutional networks and gated recurrent units is adopted. Through adaptive variational mode decomposition, time-domain and frequency-domain feature extraction, and the combination of graph convolutional networks and gated recurrent units, data processing is performed to extract hidden features and reduce the influence of interference factors.
It improves the robustness and accuracy of rolling bearing remaining life prediction, can effectively identify vibration signals from different experimental platforms, and has high prediction accuracy and generalization ability.