Sampling Methods and Experimental Research for Online Oil Monitoring in Large Diameter Oil Circuit Scenario

Wear debris in lubricating oil serves as a key indicator for characterizing the health condition of mechanical equipment, where the sampling efficiency of oil online monitoring directly affects the accuracy of fault diagnosis. Traditional vertical sampling methods suffer from significant flow field disturbance and high escape rates of micron-sized wear debris, leading to the loss of representative debris. In this study, dynamic sampling method based on flow field regulation is proposed to optimize the sampling efficiency for online monitoring in large-diameter oil circuits. The Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) numerical simulation was conducted to investigate the influence mechanism of 30°-90°sampling angles on the migration trajectories of 10–100μm wear debris. The result reveal that 60° sampling inclination angle achieves 44% capture efficiency for debris larger than 10 μm, representing 34.8% increase compared with the vertical sampling method. Particularly under high-speed conditions (1m/s), this configuration maintains stable capture rate of 44%, verifying the optimization effect of flow field regulation on wear debris kinetics. Experimental validation through joint testing with an Online Visual Ferrograph(OLVF) monitor demonstrates that, in equivalent large-diameter oil monitoring systems, the 60° inclined sampling method shows only ±5% fluctuation in the Index of Particle Coverage Area (IPCA) value across different speed ranges. This performance significantly outperforming that of vertical sampling methods. This proposed breakthrough overcomes the limitation of low sampling efficiency in traditional large-diameter oil monitoring technologies, providing high-confidence data support for oil analysis based fault diagnosis. The approach demonstrates significant engineering value for condition monitoring of large-scale mechanical equipment.