机械与电子2024，Vol.42Issue(6)：10-15,6.
基于CNN-GRU-Attention的道岔故障诊断算法研究
Research on Turnout Fault Diagnosis Algorithm Based on CNN-GRU-Attention
王凡 1甄子洋 1邓敏2
作者信息
- 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
- 折叠
摘要
Abstract
Turnout is one of the railway signal infrastructures that affects the safety of trains.By ana-lyzing 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 net-works 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 clas-sifiers for classification.In comparative experiments,multiple indicators are used to evaluate the perform-ance of this method,and the results show that this method has significant advantages in diagnostic per-formance compared to the basic methods and two other existing methods.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Key words
turnout fault diagnosis/convolutional neural network/gated recurrent unit/attention mecha-nism分类
交通工程引用本文复制引用
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(6):10-15,6.
