[1]王 凡,甄子洋,邓 敏.基于 CNN-GRU-Attention 的道岔故障诊断算法研究[J].机械与电子,2024,42(06):10-15.
 WANG Fan,ZHEN Ziyang,DENG Min.Research on Turnout Fault Diagnosis Algorithm Based on CNN-GRU-Attention[J].Machinery & Electronics,2024,42(06):10-15.
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基于 CNN-GRU-Attention 的道岔故障诊断算法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年06期
页码:
10-15
栏目:
研究与设计
出版日期:
2024-06-28

文章信息/Info

Title:
Research on Turnout Fault Diagnosis Algorithm Based on CNN-GRU-Attention
文章编号:
1001-2257 ( 2024 ) 06-0010-06
作者:
王 凡 1 甄子洋 1 邓 敏 2
1. 南京航空航天大学自动化学院,江苏 南京 211106 ;
?2. 南京轨道交通系统工程有限公司,江苏 南京 210019
Author(s):
WANG Fan1 ZHEN Ziyang1 DENG Min2
( 1.College of Automation Engineering , Nanjing University of Aeronautics and Astronautics , Nanjing 211106 , China ;
2.Nanjing Rail Transit Systems Co. , Ltd. , Nanjing 210019 , China )
关键词:
道岔故障诊断卷积神经网络门控循环单元注意力机制
Keywords:
turnout fault diagnosis convolutional neural network gated recurrent unit attention mechanism
分类号:
U284 ; TP277
文献标志码:
A
摘要:
道岔是关系列车运行安全的铁路信号基础设备之一。通过分析道岔运行过程的功率数据,可以有效判断道岔的运行状况。为实现对道岔故障自动、高效、准确的诊断,研究并提出了一种基于深度学习的故障诊断方法。首先利用卷积神经网络提取数据空间性特征,再调用门控循环单元网络提取时间性特征,再引入注意力机制对特征进行权重分配,最后使用 Softmax 分类器进行分类。在对比实验中用多种指标评定该方法的性能,结果表明,所提方法相较于基础方法和另外 2 种现有方法在诊断性能上有着显著的优势。
Abstract:
Turnout is one of the railway signal infrastructures that affects the safety of trains.By analyzing the power data of the turnout operation process , the operation status of the turnout can be effectively judged.In order to achieve automatic , efficient and accurate diagnosis of turnout faults , a fault diagnosis method based on deep learning is studied and proposed.The study first utilizes convolutional neural networks to extract spatial features from data , then calls on gated recurrent unit networks to extract temporal features , introduces attention mechanisms for allocating weights to features , and finally uses Softmax classifiers for classification.In comparative experiments , multiple indicators are used to evaluate the performance of this method , and the results show that this method has significant advantages in diagnostic performance compared to the basic methods and two other existing methods.

参考文献/References:

[ 1 ] 杨菊花,于苡健,陈光武,等 . 基于 CNN-GRU 模型的道岔故障诊断算法研究[ J ] . 铁道学报, 2020 , 42 ( 7 ):102-109.

[ 2 ] 张钉,李国宁 . 基于改进 WNN 分析功率曲线的 S700K 转辙机故障诊断[ J ] . 铁道科学与工程学报,2018 , 15( 8 ): 2123-2130.
[ 3 ] 姬文江,左元,黑新宏,等 . 基于 FastDTW 的道岔故障智能诊断方法 [ J ] . 模式识别与人工智能,2020 , 33( 11 ): 1013-1022.
[ 4 ] EKER O F , CAMCI F , KUMAR U.SVM based diagnostics on railway turnouts [ J ] .International journal of performability engineering , 2012 , 8 ( 3 ): 289-398.
[ 5 ] ATAMURADOV V , CAMCI F , BASKAN S , et al.Failure diagnostics for railway point machines using expert systems [ C ] ∥2009 IEEE International Symposium on Diagnostics for Electric Machines , Power Electronics and Drives , 2009 : 1-5.
[ 6 ] 唐维华 . 基于 LSTM / NN 的道岔故障特征提取与识别研究[ J ] . 计算机应用与软件, 2019 , 36 ( 1 ): 159-163.
[ 7 ] 池毅 . 基于深度学习的道岔故障智能诊断方法研究[ D ] . 兰州:兰州交通大学,2021.
[ 8 ] FORESTIER G , PETITJEAN F , DAU H A , et al.Generating synthetic time series to augment sparse datasets [ C ] ∥2017 IEEE International Conference on Data Mining , 2017 : 865870.
[ 9 ] 王 瑞峰,李 扬 . 基 于 1DCNN-BiLSTM 组合模型的 S700K 转辙机故障诊断 [ J ] . 电子测量与仪器学报,2022 , 36 ( 11 ): 193-200.

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备注/Memo

备注/Memo:
收稿日期: 2023-09-21
作者简介:王凡 ( 1997- ),男,江苏南京人,硕士研究生,研究方向为轨道交通故障诊断;甄子洋 ( 1981- ),男,浙江金华人,博士,教授,研究方向为舰载机/无人机着舰引导与控制、无人机集群编队协同控制与决策等。
更新日期/Last Update: 2024-07-02