高速铁路列控车载设备故障诊断的研究及应用

Study on Fault Diagnosis for Onboard Equipment in High-speed Railway Train Control Systems and Application

列控系统中车载设备故障具有复杂性和不确定性,且数据记录非文本化,传统的基于专家知识的诊断方法效率低下且精确度不佳。贝叶斯网络(BN)在处理不确定性和相关复杂性问题方面具有显著优势,本文以CTCS3-300T型车载设备为研究对象,建立贝叶斯网络模型进行故障诊断。通过分析典型车载设备故障处理现状,提出一种结合专家知识、故障数据集和K2算法的贝叶斯网络模型研究方法;利用K2算法和最大似然估计法分别进行结构学习、参数学习,从局部到整体优化贝叶斯网络诊断模型,实现故障的快速定位;建立最优贝叶斯网络模型,并进行推理计算,其故障诊断准确率为87.1%。与传统的专家知识模型相比,最优贝叶斯网络模型的故障诊断准确率提高了37.4%。经实例分析和模型验证,该模型能够保证故障诊断结果的准确性且提高故障诊断的效率。

Faults in onboard equipment within railway train control systems exhibit complexity and uncertainty,compounded by the non-textual nature of recorded data.Traditional diagnosis methods based on expert knowledge often prove inefficient and inaccurate.Bayesian network(BN)excel in handling uncertainties and related complexities.This paper focuses on CTCS3-300T onboard equipment,employing a BN model for fault diagnosis.By examining current practices in addressing typical onboard equipment malfunctions,a methodology combining expert knowledge,fault datasets,and the K2 algorithm was proposed for BN model development.Utilizing K2 algorithm and maximum likelihood estimation,structure learning and parameter learning were carried out,incrementally refining the BN diagnostic model for rapid fault localization.An optimized BN model was established,achieving a fault diagnosis accuracy of 87.1%.Compared to conventional expert-knowledge-based models,this optimal BN model enhances fault diagnosis accuracy by 37.4%.According to the results of case analysis and model validation,the model is able to ensure fault diagnosis accuracy while significantly improving diagnostic efficiency.