In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train ...In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.展开更多
列车运行控制系统车载设备(简称:列控车载设备)是一种高度集成化的电子设备,针对其维护难点,提出将故障预测及健康管理(PHM,Prognostics and Health Management)技术引入列控车载设备维护。文章基于设备全生命周期管理理念,提出列控车...列车运行控制系统车载设备(简称:列控车载设备)是一种高度集成化的电子设备,针对其维护难点,提出将故障预测及健康管理(PHM,Prognostics and Health Management)技术引入列控车载设备维护。文章基于设备全生命周期管理理念,提出列控车载设备PHM实施方案,将设备功能需求与维修需求融合一体,使列控车载设备PHM系统的研发与列控车载设备的升级改造相协调,通过列控车载设备加装升级、数据处理与分析系统建设,在完善列控车载设备BIT和数据采集与分析功能的基础上,构建列控车载设备健康评估系统。并制定了列控车载设备PHM实施计划,稳步推进相关设备研制及系统研发与建设工作,使维修保障部门能够在列控车载设备健康评估系统支持下高效协同工作,实现故障处置闭环管理,推动列控车载设备维修转向视情维修模式。展开更多
基金supported by National Natural Science Foundation of China(U2268206,T2222015)Beijing Natural Science Foundation(4232031)+1 种基金Key Fields Project of DEGP(2021ZDZX1110)Shenzhen Science and Technology Program(CJGJZD20220517141801004).
文摘In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.
文摘列车运行控制系统车载设备(简称:列控车载设备)是一种高度集成化的电子设备,针对其维护难点,提出将故障预测及健康管理(PHM,Prognostics and Health Management)技术引入列控车载设备维护。文章基于设备全生命周期管理理念,提出列控车载设备PHM实施方案,将设备功能需求与维修需求融合一体,使列控车载设备PHM系统的研发与列控车载设备的升级改造相协调,通过列控车载设备加装升级、数据处理与分析系统建设,在完善列控车载设备BIT和数据采集与分析功能的基础上,构建列控车载设备健康评估系统。并制定了列控车载设备PHM实施计划,稳步推进相关设备研制及系统研发与建设工作,使维修保障部门能够在列控车载设备健康评估系统支持下高效协同工作,实现故障处置闭环管理,推动列控车载设备维修转向视情维修模式。
