铁路通信信号工程技术2026，Vol.23Issue(8)：11-21,11.DOI:10.3969/j.issn.1673-4440.2026.08.002
列控关键设备全生命周期健康评估与延寿智能决策技术研究
Whole-Lifecycle Health Assessment and Intelligent Decision Making for Life Extension of Train Control Equipment
摘要
Abstract
In view of problems caused by the concentrated aging of train control equipment for high-speed railways and urban rail lines,such as high operation and maintenance costs,difficulty in hidden degeneration identification,and insufficiency in quantitative basis for life extension decisions,this study proposes a technology system for whole-life cycle health assessment and intelligent life extension of train control equipment to ensure proven safety and operational resilience.Taking the RAMS restraints as the preconditions and the collaborative degradation mechanism of structural,electronic,software and system functions as the core,this system utilizes the digital twin,Physics-of-Failure(POF),knowledge graph and Artificial Intelligence(AI)technologies to establish an integrated closed-loop architecture of status sensing-degradation tracing-life prediction-risk quantification-assessment of lifetime extension-auditing for decision making.From the structural,electronic,software and system dimensions,this paper proposes the technical route for multiphysics-based structural health digital twin,POF-driven electronic Prognostics and Health Management(PHM),software health management & AI compliance governance,and system functional safety & operational resilience assessment.It also presents the scenario-based implementation approach for typical devices.This study provides an overall framework and engineering practice reference that can be further verified by means of subsequent experiments;and its prediction performance and life-extension benefits need to be further assessed in view of specific device type and field data.关键词
人工智能/列控设备/健康状态评估/智能延寿/数字孪生/RAMS/软件健康管理/运营韧性Key words
artificial intelligence/train control equipment/health state assessment/intelligent life extension/digital twin/RAMS/software health management/operational resilience分类
信息技术与安全科学引用本文复制引用
李紫时,彭开香,乔志超,李彦锋,张瀚文,刘俊囡,李涵蕊,孙超..列控关键设备全生命周期健康评估与延寿智能决策技术研究[J].铁路通信信号工程技术,2026,23(8):11-21,11.基金项目
国家重点研发计划课题项目(2022YFB4300603) （2022YFB4300603）
