首页|基于BERT+CNN_BiLSTM的列控车载设备故障诊断

基于BERT+CNN_BiLSTM的列控车载设备故障诊断

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列控车载设备作为列车运行控制系统核心设备,在高速列车运行过程中发挥着重要作用.目前,其故障诊断仅依赖于现场作业人员经验,诊断效率相对较低.为了实现列控车载设备故障自动诊断并提高诊断效率,提出了BERT+CNN_BiLSTM故障诊断模型.首先,使用来自变换器的双向编码器表征量(Bidirectional encoder representations from transformers,BERT)模型将应用事件日志(Application event log,AElog)转换为计算机能够识别的可以挖掘语义信息的文本向量表示.其次,分别利用卷积神经网络(Convolutional neural network,CNN)和双向长短时记忆网络(Bidirectional long short-term memory,BiLSTM)提取故障特征并进行组合,从而增强空间和时序能力.最后,利用Softmax实现列控车载设备的故障分类与诊断.实验中,选取一列实际运行的列车为研究对象,以运行过程中产生的AElog日志作为实验数据来验证BERT+CNN_BiLSTM模型的性能.与传统机器学习算法、BERT+BiLSTM模型和BERT+CNN模型相比,BERT+CNN_BiLSTM模型的准确率、 召回率和F1分别为92.27％、91.03％ 和91.64％,表明该模型在高速列车控制系统故障诊断中性能优良.
Fault diagnosis for on-board equipment of train control system based on BERT+CNN_BiLSTM
The on-board equipment as core equipment of train control system plays an important role in the process of high-speed train operation. At present, its fault diagnosis only depends on the experience of on-site operators and diagnosis efficiency is relatively low. To realize automatic fault diagnosis and improve diagnosis efficiency of the on-board equipment of train control system, a fault diagnosis model called BERT+CNN_BiLSTM was proposed, which combined bidirectional encoder representations from transformers(BERT) model, convolutional neural network(CNN) and bidirectional long short-term memory(BiLSTM). Firstly, the BERT model was used to transform the application event log(AElog) into a text vector representation that can mine semantic information recognized by computer. Secondly, CNN and BiLSTM were used to extract fault features and combine them to enhance spatial and temporal capability of the model. Finally, fault classification and diagnosis of on-board equipment of train control system was realized by using Softmax. In the experiment, taking an actual on-board equipment as the research object, the AElog generated during the train operation was selected as texperimental data to verify the performance of BERT+CNN_BiLSTM model. The results showed that compared with traditional machine learning algorithm, BERT+BiLSTM model and BERT+CNN model, the pecision, recall and F1 of BERT+CNN_BiLSTM model were 92.27％, 91.03％ and 91.64％, respectively, which indicates that the proposed BERT+CNN_BiLSTM model has a better overall performance in the fault diagnosis of on-board equipment of high-speed train control system.

on-board equipmentfault diagnosisbidirectional encoder representations from transformers(BERT)application event log (AElog)bidirectional long short-term memory(BiLSTM)convolutional neural network(CNN)

陈永刚、贾水兰、朱键、韩思成、熊文祥

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兰州交通大学自动化与电气工程学院,甘肃兰州 730070

中国铁路乌鲁木齐局集团有限公司阿勒泰基础设施段,新疆阿勒泰 836500

车载设备 故障诊断 来自变换器的双向编码器表征量 应用事件日志 双向长短时记忆网络 卷积神经网络

National Natural Science Foundation of China

52062028

2024

10.62756/jmsi.1674-8042.2024012

测试科学与仪器
中北大学

测试科学与仪器

影响因子：0.111
ISSN：1674-8042
年,卷(期)：2024.15(1)
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