Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a f...Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.展开更多
客车制动缸膜板的稳定工作对制动系统的安全性和可靠性具有决定性意义。制动缸膜板直径较大,密封圈处螺栓数量较多且工况复杂,分析表明其安装方式及温度对密封性能影响显著。文中采用有限元分析方法研究顺次加载、ASME PCC-1加载、JIS B...客车制动缸膜板的稳定工作对制动系统的安全性和可靠性具有决定性意义。制动缸膜板直径较大,密封圈处螺栓数量较多且工况复杂,分析表明其安装方式及温度对密封性能影响显著。文中采用有限元分析方法研究顺次加载、ASME PCC-1加载、JIS B 2251加载方式对螺栓预紧力的影响。同时分析了温度变化对螺栓预紧力以及膜板密封面应力分布的影响,并且对制动缸进行了不同温度下的密封性能试验。结果表明,3种加载方式最后都能得到较为均匀的载荷,其中JIS B 2251加载方式最接近目标载荷,加载过程最简易;随着温度的升高,螺栓预紧力上升,密封面上的接触应力增大;低温状态下制动缸内压力下降最快,高温次之,常温最慢。展开更多
基金supported by the CRRC Original Technology TenYear Cultivation Program(Grant No.2022CYY007)。
文摘Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.
文摘客车制动缸膜板的稳定工作对制动系统的安全性和可靠性具有决定性意义。制动缸膜板直径较大,密封圈处螺栓数量较多且工况复杂,分析表明其安装方式及温度对密封性能影响显著。文中采用有限元分析方法研究顺次加载、ASME PCC-1加载、JIS B 2251加载方式对螺栓预紧力的影响。同时分析了温度变化对螺栓预紧力以及膜板密封面应力分布的影响,并且对制动缸进行了不同温度下的密封性能试验。结果表明,3种加载方式最后都能得到较为均匀的载荷,其中JIS B 2251加载方式最接近目标载荷,加载过程最简易;随着温度的升高,螺栓预紧力上升,密封面上的接触应力增大;低温状态下制动缸内压力下降最快,高温次之,常温最慢。
