A Graph-Data-Based Monitoring Method of Bearing Lubrication Using Multi-Sensor

Super-precision bearing lubrication condition is essential for equipment’s overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status.