| 李 斌,阎君宇.基于 GAF-CNN 的弓网电弧识别方法研究[J].电子测量与仪器学报,2022,36(1):188-195 |
| 基于 GAF-CNN 的弓网电弧识别方法研究 |
| Research on recognition method of pantograph arc based on GAF-CNN |
| |
| DOI： |
| 中文关键词: 滑动电接触 图像编码 格拉姆角场 卷积神经网络 |
| 英文关键词:sliding electrical contact image coding Gram angle field convolutional neural network |
| 基金项目:国家自然科学基金(51674136)、辽宁工程技术大学生产技术问题创新研究基金(20160019T)项目资助 |
|
|
|
|
| 摘要点击次数: 502 |
| 全文下载次数: 679 |
| 中文摘要: |
| 由于高速铁路接触网会产生弓网电弧对弓网系统有危害,为了减少弓网损害。 提出一种电流时间序列编码技术,即格
拉姆角求和/ 差分场(GASF / GADF)。 由于不同受流状态的电流信号不同,其时间序列编码形成的图像也不同,这使得计算机
视觉技术可以用于时间序列分类,用来识别弓网电弧。 共进行了 5 组不同条件下的弓网受流实验,测量得到不同条件下弓网系
统中的电流数据,并将弓网实验得到的电流数据的状态分为正常受流状态和电弧受流状态。 通过构建神经网络,提取电弧电流
信号,以格拉姆角场(GAF)图像的形式直观展示了卷积神经网络(CNN)对弓网电弧数据的抽象特征提取情况。 实验结果表
明,该方法可在不同条件下准确识别弓网电弧避免视频图像背景变化的问题为弓网电弧故障识别提供一种思路。 |
| 英文摘要: |
| Since the catenary of high-speed railway will produce pantograph arc, it is harmful to pantograph system, in order to reduce
pantograph damage. A current time series coding technology, namely, the Gram angle summation / differential field (GASF/ GADF) is
proposed. Because the current signals of different current receiving states are different, the images formed by their time series coding are
also different, which makes computer vision technology can be used for time series classification to identify pantograph arcs. A total of
five groups of pantograph receiving experiments were carried out under different conditions to measure the current data in pantograph
system under different conditions, and the current data obtained from pantograph experiments were divided into normal receiving state
and arc receiving state. By constructing a neural network and extracting the arc current signal, it visually demonstrates the abstract
feature extraction of the CNN from the arch-net arc data in the form of a Gram angle field (GAF) image. The experimental results show
that the method in this paper can accurately identify pantograph and network arcs under different conditions, avoiding the problem of
video image background changes, and provides an idea for pantograph and network arc fault identification. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
