Leakage prediction approach and influencing factor analysis from seal test

Purpose Leakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions. Design/methodology/approach Seal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated. Findings The results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence. Originality/value This work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

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Publication Details

Journal
Industrial Lubrication and Tribology
Published
2024-11-13
DOI
https://doi.org/10.1108/ilt-07-2024-0271
Citations
1
Primary Topic
Tribology and Lubrication Engineering
Type
article
Field-Weighted Citation Impact
0.21
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article

Leakage prediction approach and influencing factor analysis from seal test

Ran Gong, Huajun Che, Jin Xu, Jinxiao Li et al.
1 citations
Industrial Lubrication and Tribology
Tribology and Lubrication Engineering
0.21
article

Leakage prediction approach and influencing factor analysis from seal test

Ran Gong, Huajun Che, Jin Xu, Jinxiao Li, He Zhang
article en
1 citations

Abstract

Purpose Leakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions. Design/methodology/approach Seal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated. Findings The results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence. Originality/value This work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

Industrial Lubrication and TribologyVol. 77(1)
Jiangsu University (CN)
Life in Land
Openalex Percentile: Top 50%
Tribology and Lubrication Engineering
0.21
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Leakage prediction approach and influencing factor analysis from seal test — Ran Gong, Huajun Che, et al. · Industrial Lubrication and Tribology (2024) | TGRS Research Map | TGRS