CAJ | 학술논문

本文以故障文本信息为依据，提出基于文本挖掘的高铁信号系统车载设备的故障诊断方法。针对故障追踪表记录的不规范性和随意性，采用主题模型对故障追踪表进行分析和特征提取；在此基础上，考虑到高铁信号系统车载设备故障诊断的不确定性，采用贝叶斯网络作为故障分类的方法。在贝叶斯网络结构的确定中，根据车载设备的特点与领域专家知识，提出适用于车载设备的贝叶斯结构学习算法 HDBN＿SL。以武广线的现场数据为依据，进行实验分析，测试结果表明本文特征提取以及故障诊断方法具有较好的诊断准确性。
본문이고장문본신식위의거，제출기우문본알굴적고철신호계통차재설비적고장진단방법。침대고장추종표기록적불규범성화수의성，채용주제모형대고장추종표진행분석화특정제취；재차기출상，고필도고철신호계통차재설비고장진단적불학정성，채용패협사망락작위고장분류적방법。재패협사망락결구적학정중，근거차재설비적특점여영역전가지식，제출괄용우차재설비적패협사결구학습산법 HDBN＿SL。이무엄선적현장수거위의거，진행실험분석，측시결과표명본문특정제취이급고장진단방법구유교호적진단준학성。
Based on fault text data ,a fault diagnosis method for vehicle on‐board equipment (VOBE) of high speed railway signal system has been proposed .Due to the irregularity and arbitrary nature of fault tracing re‐cords ,the topic model was used for fault analysis and feature extraction .In addition ,considering the uncer‐tainty and complexity of fault diagnosis of VOBE of high speed railway signal system ,a Bayesian network (BN) based fault diagnosis system for VOBE was proposed .In the process of deriving an appropriate BN struc‐ture for VOBE ,subject to the characteristics and domain knowledge of VOBE ,a new algorithm called HDBN_SL (Hierarchical Diagnostic Bayesian Networks‐Structure Learning ) that is applicable to VOBE was proposed . Experimental analysis was conducted based on the field data from Wuhan‐Guangzhou high speed railway signa‐ling systems .The test results showed that the text mining feature extraction and fault diagnosis method deliv‐ered better diagnostic accuracy .