The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board...The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.展开更多
为提高CTCS3-300T车载设备系统可用性,保障高铁动车组安全高效运行,进行CTCS3-300T车载设备应答器传输模块(Balise Transmission Module,BTM)单元热备冗余研究。通过对既有CTCS3-300T车载设备系统架构、硬件基础和软件基础进行研究,提出...为提高CTCS3-300T车载设备系统可用性,保障高铁动车组安全高效运行,进行CTCS3-300T车载设备应答器传输模块(Balise Transmission Module,BTM)单元热备冗余研究。通过对既有CTCS3-300T车载设备系统架构、硬件基础和软件基础进行研究,提出CTCS3-300T车载设备BTM单元热备冗余方案。通过优化BTM单元、C3等级核心控车单元(ATP Control Unit,ATPCU)和C2等级核心控车单元(C2 Control Unit,C2CU),实现BTM热备功能下的BTM自检、BTM天线控制、BTM报文使用和BTM故障处理等功能处理。并进行双BTM同时工作及主机软件和BTM软件变更的风险分析,完成BTM单元热备冗余功能测试验证,论证BTM单元热备冗余方案有效可用。展开更多
基金supported by the Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project(Grant No.2022YFB4300500).
文摘The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.
文摘为提高CTCS3-300T车载设备系统可用性,保障高铁动车组安全高效运行,进行CTCS3-300T车载设备应答器传输模块(Balise Transmission Module,BTM)单元热备冗余研究。通过对既有CTCS3-300T车载设备系统架构、硬件基础和软件基础进行研究,提出CTCS3-300T车载设备BTM单元热备冗余方案。通过优化BTM单元、C3等级核心控车单元(ATP Control Unit,ATPCU)和C2等级核心控车单元(C2 Control Unit,C2CU),实现BTM热备功能下的BTM自检、BTM天线控制、BTM报文使用和BTM故障处理等功能处理。并进行双BTM同时工作及主机软件和BTM软件变更的风险分析,完成BTM单元热备冗余功能测试验证,论证BTM单元热备冗余方案有效可用。
