Tribological Characteristics of Rotary Vane Steering Gear Seals Under the Oil With Different Abrasive Particle Sizes

Abrasive particles often cause wear of the rotary vane seals during an operation process, and lead to seal failure and mechanical fault. Focusing on the wear mechanisms and extracting the tribological information during the friction are the prerequisites for extending the service life and carrying out the fault diagnosis prediction of rotary vane seals. Four different sizes of iron particles were selected to mix with the same volume of hydraulic oil to explore the effect of lubricating oil containing abrasive particles on the wear behaviors of the seal. The tribological behaviors during friction were comprehensively analyzed, including the coefficient of friction (COF), wear volume, surface topography, vibration, noise, and other information. The results demonstrated that the wear characteristics of the seal's contact surfaces changed obviously. The wear morphologies, coefficients of friction, surface roughnesses, and vibration signals increased with the increase of the abrasive particles' size, with the wear state gradually worsening. When the iron particle size reached 160 mu m, the COF increased by 0.047, and the peak vibration signal increased from 4.31 to 36.97 m/s2, compared with pure hydraulic oil lubrication. This tribological information could disclose the wear mechanisms of thermoplastic polyurethanes (TPU) and reveal the TPU wear states. Determining the tribological information for the rubbing pairs was a potential and effective way for evaluating or predicting the rotary vane steering gear seals' wear states or failure modes. This study lays the foundation for understanding the wear mechanisms and the fault diagnosis of steering gear seals.

Automated summary

Abrasive particles can cause wear and failure of rotary vane seals. Analyzing the tribological information during friction can reveal the wear mechanisms and provide a foundation for extending the service life and carrying out fault diagnosis prediction.