| 林海香,卢 冉,陆人杰,许 丽,赵正祥,白万胜.基于 LDA-CLCBA 组合模型的
高速铁路道岔故障诊断[J].电子测量与仪器学报,2022,36(3):251-259 |
| 基于 LDA-CLCBA 组合模型的
高速铁路道岔故障诊断 |
| Fault diagnosis for turnout of high-speed railway basedon LDA-CLCBA hybrid model |
| |
| DOI： |
| 中文关键词: ZY(J)7 道岔 故障诊断 LDA 主题模型 关联规则分类 类支持度 类支持度阈值 |
| 英文关键词:ZY(J)7 turnout fault diagnosis LDA topic model association classification class support class support threshold |
| 基金项目:甘肃省高等学校创新基金项目(2020B 104)、2021年度甘肃省优秀研究生“创新之星”项目(2021CXZX 606)资助 |
|
|
|
|
| 摘要点击次数: 827 |
| 全文下载次数: 1009 |
| 中文摘要: |
| ZY(J)7 电液道岔转换设备已在高速铁路大量投入使用,对其进行精确的故障诊断有助于高速铁路道岔的日常维护作
业。 以 ZY(J)7 道岔故障文本数据作为研究对象,提出一种基于 LDA( latent dirichlet allocation)主题模型与关联规则分类技术
相结合的高速铁路道岔故障诊断模型。 该模型首先采用 LDA 主题模型实现 ZY(J)7 道岔故障文本数据的特征提取;其次,由于
道岔各故障类别数据的不均衡性,将原有的关联规则分类算法引入类支持度相关概念进行不平衡数据的处理,最终实现
ZY(J)7 道岔的故障诊断。 通过对某铁路局 2017~ 2019 年的 ZY(J)7 道岔故障文本数据进行实验分析,实验结果表明提出的故
障诊断方法分类精确率和召回率分别达到 95. 08%和 90. 24%,既保证了整体分类的准确率又有较好的小类别分类性能。 |
| 英文摘要: |
| ZY(J)7 electrohydraulic turnout switch equipment has been widely used in high-speed railway, and accurate fault diagnosis is
helpful to the daily maintenance of high-speed railway turnout. Taking the fault text data of ZY( J) 7 turnout as the research object, a
fault diagnosis model for high-speed railway turnout was proposed, which combined LDA topic model with association rules classification
technology. Firstly, this model adopted LDA topic model to extract the feature of ZY(J)7 turnout fault text data. Secondly, due to the
unbalanced data of each fault type of turnout, the original association rule classification algorithm was introduced into the concept of class
support to deal with unbalanced data, and finally the fault diagnosis of ZY(J)7 switch was realized. Through the experimental analysis of
ZY(J)7 turnout fault text data of a railway bureau from 2017 to 2019, the experimental results indicate that the classification precision
and recall rate of the proposed fault diagnosis method are 95. 08% and 90. 24% respectively, which not only guarantees the accuracy of
the whole classification, but also gets better classification performance of minority class. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
