Analysis of the Temperature Characteristics of High?speed Train Bearings Based on a Dynamics Model and Thermal Network Method

• Published in: 中国机械工程学报 Vol. 35; no. 5; pp. 20 - 32
• Main Authors: Baosen Wang, Yongqiang Liu, Bin Zhang, Wenqing Huai
• Format: Journal Article
• Language: English
• Published: School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China 2022; Department of Electrical Engi-neering,University of South Carolina,Columbia 29201,USA%School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China%Department of Electrical Engi-neering,University of South Carolina,Columbia 29201,USA%Taiyuan Vehicle Depot of Daqin Railway Co.,Ltd.,Taiyuan 030000,China; School of Traffic and Transportation,Shijiazhuang Tiedao University,Shiji-azhuang 050043,China
• Subjects: Temperature characteristics; Thermal network method; High-speed train; Axle box bearing
• ISSN: 1000-9345

High-speed trains often use temperature sensors to monitor the motion state of bearings. However, the temperature of bearings can be affected by factors such as weather and faults. Therefore, it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors. In this study, a dynamics model of the axle box bearing of high-speed trains is established. The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring, inner-ring, and rolling-element faults. Based on the model, a thermal network method is introduced to study the temperature field distribution of the axle box bearings of high-speed trains. In this model, the heat generation, conduction, and dispersion of the isothermal nodes can be solved. The results show that the temperature of the contact point between the outer-ring raceway and rolling- elements is the highest. The relationships between the node temperature and the speed, fault type, and fault size are analyzed, finding that the higher the speed, the higher the node temperature. Under different fault types, the node temperature first increases and then decreases as the fault size increases. The effectiveness of the model is demon- strated using the actual temperature data of a high-speed train. This study proposes a thermal network model that can predict the temperature of each component of the bearings on a high-speed train under various speed and fault conditions.