An accurate and objective assessment of the health status of EMU trains is of great importance.In order to make sure the trains are functional,reliable,and endurable in their full life cycle(FLC),health assessment met...An accurate and objective assessment of the health status of EMU trains is of great importance.In order to make sure the trains are functional,reliable,and endurable in their full life cycle(FLC),health assessment method for EMU trains after Level 3-5 maintenance and repair is studied.First,the element-selection principles and the assessment rules are defined;second,to present the complex topological relationship between the elements in assessment,a functional logical structure construction method is proposed;third,a health value calculation model is defined based on the element’s characteristics and their logical structures.The health variables of each element is calculated and fitted following the steps in the corresponding weight calculation methods.The assessment method is proved to be applicable and effective.展开更多
This paper studies the structural response of high-speed train wipers under the combined action of complex flow fields and scraping actions.The stress concentration areas are determined through simulation analysis,and...This paper studies the structural response of high-speed train wipers under the combined action of complex flow fields and scraping actions.The stress concentration areas are determined through simulation analysis,and the stress and aerodynamic load measurement points are reasonably arranged accordingly.The actual measurement is carried out in combination with the operating conditions of the existing lines.The stress variations and spectral characteristics of the train under different speed levels(80,160,180,200 km/h),tunnel entry and exit,and scraper action conditions were compared and analyzed.The stress amplification factors under tunnel intersection and scraper action were obtained,providing boundary conditions for the design of wipers for highspeed s.The research results show that the maximum stress of the wiper structure obtained through simulation calculation is concentrated at the connection of the wiper arm.Structural stress increases with the rise of speed grade.The stress increases by 1.11 times when the tunnel meets.When the scraper operates,the stress on the scraper arm increases by 4.1–7.6 times.Due to the broadband excitation effect of the aerodynamic load,the spectral energy of the structure is relatively high at the natural frequency,which excites the natural mode of the wiper.展开更多
故障预测与健康管理(prognostics and health management,PHM)技术应用于动车组关键部件监控以来,在保证动车组运行安全、指导动车组检修等方面起到了重要作用。PHM系统根据动车组技术发展、现场应用实际,其功能、模型也在不断优化中。...故障预测与健康管理(prognostics and health management,PHM)技术应用于动车组关键部件监控以来,在保证动车组运行安全、指导动车组检修等方面起到了重要作用。PHM系统根据动车组技术发展、现场应用实际,其功能、模型也在不断优化中。结合某动车段现场应用实际及需求,对基于动车组PHM技术的健康监测及专家支持系统进行功能优化。通过优化动车组空调、变压器、变流器、牵引电机等关键部件预警预测模型阈值,增加模型逻辑展示、一键生成用户要求格式的故障信息、动车组部件全景展示等功能,实现个性化定制预警预测模型、快速传递故障信息、动车组部件可视化辅助应急指导等一系列智能化监控,达到故障超前预判、提升快速响应能力,降低动车组故障率,减少对行车秩序影响的目的。展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
文摘An accurate and objective assessment of the health status of EMU trains is of great importance.In order to make sure the trains are functional,reliable,and endurable in their full life cycle(FLC),health assessment method for EMU trains after Level 3-5 maintenance and repair is studied.First,the element-selection principles and the assessment rules are defined;second,to present the complex topological relationship between the elements in assessment,a functional logical structure construction method is proposed;third,a health value calculation model is defined based on the element’s characteristics and their logical structures.The health variables of each element is calculated and fitted following the steps in the corresponding weight calculation methods.The assessment method is proved to be applicable and effective.
文摘This paper studies the structural response of high-speed train wipers under the combined action of complex flow fields and scraping actions.The stress concentration areas are determined through simulation analysis,and the stress and aerodynamic load measurement points are reasonably arranged accordingly.The actual measurement is carried out in combination with the operating conditions of the existing lines.The stress variations and spectral characteristics of the train under different speed levels(80,160,180,200 km/h),tunnel entry and exit,and scraper action conditions were compared and analyzed.The stress amplification factors under tunnel intersection and scraper action were obtained,providing boundary conditions for the design of wipers for highspeed s.The research results show that the maximum stress of the wiper structure obtained through simulation calculation is concentrated at the connection of the wiper arm.Structural stress increases with the rise of speed grade.The stress increases by 1.11 times when the tunnel meets.When the scraper operates,the stress on the scraper arm increases by 4.1–7.6 times.Due to the broadband excitation effect of the aerodynamic load,the spectral energy of the structure is relatively high at the natural frequency,which excites the natural mode of the wiper.
文摘故障预测与健康管理(prognostics and health management,PHM)技术应用于动车组关键部件监控以来,在保证动车组运行安全、指导动车组检修等方面起到了重要作用。PHM系统根据动车组技术发展、现场应用实际,其功能、模型也在不断优化中。结合某动车段现场应用实际及需求,对基于动车组PHM技术的健康监测及专家支持系统进行功能优化。通过优化动车组空调、变压器、变流器、牵引电机等关键部件预警预测模型阈值,增加模型逻辑展示、一键生成用户要求格式的故障信息、动车组部件全景展示等功能,实现个性化定制预警预测模型、快速传递故障信息、动车组部件可视化辅助应急指导等一系列智能化监控,达到故障超前预判、提升快速响应能力,降低动车组故障率,减少对行车秩序影响的目的。
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
