A Study on the Influence of Electrical Discharges on the Formation of White Etching Cracks in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique

被引:4
|
作者
Esmaeili, Kamran [1 ]
Wang, Ling [1 ]
Harvey, Terry J. [1 ]
White, Neil M. [2 ]
Holweger, Walter [1 ]
机构
[1] Univ Southampton, Fac Engn & Phys Sci, Natl Ctr Adv Tribol Southampton nCATS, Southampton SO17 1BJ, England
[2] Univ Southampton, Fac Engn & Phys Sci, Elect & Comp Sci, Southampton SO17 1BJ, England
关键词
electrostatic sensor; voltage measurement technique; electrical discharges; WEC formation; quantification algorithm; detection and diagnosis; EDGE STRUCTURE SPECTROSCOPY; MICROSTRUCTURAL CHANGES; FATIGUE; ZDDP; INCLUSIONS; MECHANISM;
D O I
10.3390/lubricants11040164
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In bearing applications, the presence of stray and parasitic currents in combination with lubricants has been studied for almost a century and has been found to cause fluting and corrugation damages under high current densities. However, recent research has suggested that at low current densities (<1 mA/mm(2)) under specific operating conditions, electrical discharges can substantially reduce bearing life due to the formation of white etching cracks (WECs). To date, limited studies have investigated the critical operating and electrical conditions for WEC formation and demonstrated effective fault detection techniques. This study uses a novel monitoring technique known as the electrostatic sensing technique to detect, monitor and characterise electrical discharges in an oil-lubricated steel-steel rolling contact on a TE74 twin-roller machine. The findings demonstrate that WECs can be formed under the influence of electrical discharges in less than 50 h, and the electrostatic sensors are effective for the early detection of critical electrical discharges related to WEC-induced failures.
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页数:28
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  • [1] Electrical Discharges in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique
    Esmaeili, Kamran
    Wang, Ling
    Harvey, Terry J.
    White, Neil M.
    Holweger, Walter
    SENSORS, 2022, 22 (01)