基于字词融合的高铁道岔多级故障诊断组合模型

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。

Combined model for multi-level fault diagnosis of high-speedrail turnouts based on character and word fusion

To effectively improve the maintenance efficiency and fault location accuracy of high-speed railway turnouts, a combinedmodel for multi-level fault diagnosis of high-speed rail turnouts based on character and word fusion was proposed. Firstly, a professionalthesaurus of high-speed rail turnout equipment was established, and fault texts were represented as character vectors and word vectorsand 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, acombination of Bi-directional long short-term memory ( BiLSTM) and convolutional neural network ( CNN) was used to extract deepfeatures of the fault text. Finally, an intelligent diagnosis of faults was achieved by means of a classifier. The model performance wasvalidated using fault text data of China high-speed railway turnout faults. The test results show that the accuracy of the proposed modelreaches 95. 62% for the primary fault diagnosis and 93. 81% for the secondary fault diagnosis, which proves that the multi-level faultdiagnosis accuracy can reach the desired effect.