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
分布式道德责任(distributed moral responsibility,DMR)的概念由来已久。如果将分布式道德责任的诠释彻底还原为(某些)人、个体和负道德责任的行为(morally loaded actions)总和时,那么DMR的分配,无论是赞赏和奖励或谴责和惩罚,从概念...分布式道德责任(distributed moral responsibility,DMR)的概念由来已久。如果将分布式道德责任的诠释彻底还原为(某些)人、个体和负道德责任的行为(morally loaded actions)总和时,那么DMR的分配,无论是赞赏和奖励或谴责和惩罚,从概念的角度而言是没有问题的,但从实际操作上则是非常困难的。然而,在分布式的环境中,代理网络、某些人类、人工体(譬如程序)和混合体(譬如基于一个软件平台在一起工作的一群人)都可能带来分布式道德行为(distributed moral actions,DMAs)。DMAs是有善恶之分(即,负载道德的)的行为,它们是由一些其自身既非善也非恶(道德中立的)的局部交互所导致的。在本文中,我将分析由DMAs而引发的DMRs,并为完全道德责任(无过失责任)默认地分配给网络中所有节点/代理提供辩护,这些节点/代理对于所探讨的DMA在因果上相关,但与其意向性无关。所提出的机制受如下三个概念的启发,并从中汲取了相关内容:网络理论中的反向传播(back propagation from network theory)、法理学中的严格责任原则(strict liability from jurisprudence)和认识论逻辑中的常识(common knowledge from epistemic logic)。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.
文摘分布式道德责任(distributed moral responsibility,DMR)的概念由来已久。如果将分布式道德责任的诠释彻底还原为(某些)人、个体和负道德责任的行为(morally loaded actions)总和时,那么DMR的分配,无论是赞赏和奖励或谴责和惩罚,从概念的角度而言是没有问题的,但从实际操作上则是非常困难的。然而,在分布式的环境中,代理网络、某些人类、人工体(譬如程序)和混合体(譬如基于一个软件平台在一起工作的一群人)都可能带来分布式道德行为(distributed moral actions,DMAs)。DMAs是有善恶之分(即,负载道德的)的行为,它们是由一些其自身既非善也非恶(道德中立的)的局部交互所导致的。在本文中,我将分析由DMAs而引发的DMRs,并为完全道德责任(无过失责任)默认地分配给网络中所有节点/代理提供辩护,这些节点/代理对于所探讨的DMA在因果上相关,但与其意向性无关。所提出的机制受如下三个概念的启发,并从中汲取了相关内容:网络理论中的反向传播(back propagation from network theory)、法理学中的严格责任原则(strict liability from jurisprudence)和认识论逻辑中的常识(common knowledge from epistemic logic)。
