摘要
为有效提升高速铁路道岔维护效率和故障定位准确率,面向其故障文本数据,提出了一种基于字词融合的高速铁路道岔多级故障诊断组合模型。首先,建立高速铁路道岔专业词库,将文本表示为字向量与词向量并进行深度融合。其次,考虑到故障文本存在类别不均衡问题,采用Borderline-SMOTE算法对不均衡文本数据进行处理,优化故障文本数据分布。接着使用BiLSTM(Bi-directional long short-term memory)-CNN(convolutional neural network)的组合神经网络提取故障文本深度特征,最后通过分类器实现智能故障诊断。采用我国高速铁路道岔故障文本数据进行模型性能验证,结果显示所提模型的一级故障诊断准确率达到95.62%,二级故障诊断准确率达到93.81%,证明多级故障诊断精度可达到理想效果。
To effectively improve the maintenance efficiency and fault location accuracy of high-speed railway turnouts,a combined model for multi-level fault diagnosis of high-speed rail turnouts based on character and word fusion was proposed.Firstly,a professional thesaurus of high-speed rail turnout equipment was established,and fault texts were represented as character vectors and word vectors and the character vectors and word vectors were deeply fused.Secondly,considering the problem of imbalanced categories in fault texts,the Borderline-SMOTE algorithm was used to process the imbalanced text data to optimize the fault text data distribution.Then,a combination of Bi-directional long short-term memory(BiLSTM)and convolutional neural network(CNN)was used to extract deep features of the fault text.Finally,an intelligent diagnosis of faults was achieved by means of a classifier.The model performance was validated using fault text data of China high-speed railway turnout faults.The test results show that the accuracy of the proposed model reaches 95.62%for the primary fault diagnosis and 93.81%for the secondary fault diagnosis,which proves that the multi-level fault diagnosis accuracy can reach the desired effect.
作者
林海香
赵正祥
陆人杰
卢冉
白万胜
胡娜娜
Lin Haixiang;Zhao Zhengxiang;Lu Renjie;Lu Ran;Bai Wansheng;Hu Nana(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;CASCO Signal Ltd,Shanghai 200071,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第10期217-226,共10页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61763023)
甘肃省科技计划项目(20YF8GA037)
甘肃省高等学校创新基金(2020B-104)项目资助
作者简介
通信作者:林海香,分别在2000年和2007年于兰州交通大学获得学士学位和硕士学位,2020年于同济大学获得博士学位,现为兰州交通大学副教授,主要研究方向为交通信息数据挖掘。E-mail:linhaixiang@mail.lzjtu.cn;Corresponding author:赵正祥,现为兰州交通大学硕士研究生,主要研究方向为自然语言处理。E-mail:511229689@qq.com
