摘要
列车运行控制系统是高速列车信息控制系统的"神经中枢",其结构复杂、组件繁多,其中,车载子系统是列车运行控制系统的核心组成部分,是保证行车安全、提高运行效率的关键.目前,车载子系统的故障数据处理方式仍以人工处理实现故障定位为主,尚未深入到系统故障机理层面,无法实现有效的故障预测.本文分析了车载子系统的结构及系统故障处置现状,总结了现存的主要问题,结合车载子系统结构及各模块性能参数,提出了基于贝叶斯网络(Bayesian network, BN)的系统级故障预测模型构建方法.利用实际系统现场运行数据,采用基于贝叶斯网络的方法进行故障预测,分别在20, 200, 2000,20000组数据条件下实施了验证,故障预测准确率分别为5%, 27%, 92%, 96.3%,在2000组数据条件下同时对隐马尔科夫模型(hidden Markov model, HMM)、神经网络(neural network, NN)与本文所提出的方法进行了对比,预测结果验证了贝叶斯网络在系统级故障预测方面的显著优势.
The train control system is the "nerve center" of the high-speed train information system, which is large-scale and comprises various components. The on-board subsystem is the core of the train control system and is key to ensuring traffic safety and improving operating efficiency. Currently, the fault data processing methods of the on-board subsystem remain manual, which primarily realizes the fault detection and location.It is difficult to achieve the fault mechanism level, and fault prediction cannot be realized effectively. In this paper, the on-board subsystem structure and the fault disposal status were analyzed. The existing problems have been summarized, and some concepts and algorithms to predict faults were introduced. Based on the on-board subsystem structure and each module’s performance parameters, the system-level fault prediction model was established. Based on the practical operational data sets, the fault prediction based on the Bayesian network was carried out and verified under 20, 200, 2000 and 20000 sets, respectively. The prediction accuracy was 5%,27%, 92% and 96.3%, respectively, under the condition of 2000 data sets. The hidden Markov model and neural network-based fault prediction solutions were compared with the proposed method. The results demonstrate the advanced performance of the Bayesian network-based solution in system-level fault prediction.
作者
臧钰
蔡伯根
上官伟
王化深
Yu ZANG;Baigen CAI;Wei SHANGGUAN;Huashen WANG(School of Electronics and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China)
出处
《中国科学：信息科学》
CSCD
北大核心
2020年第4期511-526,共16页
Scientia Sinica(Informationis)
基金
国家自然科学基金重大项目(批准号:61490705)资助。
