| 林海香,赵正祥,陆人杰,卢 冉,白万胜,胡娜娜.基于字词融合的高铁道岔多级故障诊断组合模型[J].电子测量与仪器学报,2022,36(10):217-226 |
| 基于字词融合的高铁道岔多级故障诊断组合模型 |
| Combined model for multi-level fault diagnosis of high-speedrail turnouts based on character and word fusion |
| |
| DOI： |
| 中文关键词: 高速铁路道岔 多级故障诊断 字词融合 Borderline-SMOTE 组合神经网络 |
| 英文关键词:high-speed railway turnout equipment multi-level intelligent diagnosis character and word fusion Borderline-SMOTE combined neural network |
| 基金项目: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 |
|
|
|
|
| 摘要点击次数: 697 |
| 全文下载次数: 976 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
