[1]许晓路,周 文,周东国,等.基于PCNN分层聚类迭代的故障区域自动提取方法[J].红外技术,2020,42(8):809-814.[doi:10.11846/j.issn.1001_8891.202008017]
 XU Xiaolu,ZHOU Wen,ZHOU Dongguo,et al.Automatic Fault Region Extraction Using PCNN Hierarchical Clustering[J].Infrared Technology,2020,42(8):809-814.[doi:10.11846/j.issn.1001_8891.202008017]
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基于PCNN分层聚类迭代的故障区域自动提取方法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第8期
页码:
809-814
栏目:
出版日期:
2020-08-23

文章信息/Info

Title:
Automatic Fault Region Extraction Using PCNN Hierarchical Clustering
文章编号:
1001-8891(20)08-0809-06
作者:
许晓路12周 文12周东国3朱诗沁12倪 辉12罗传仙12
1. 南瑞集团(国网电力科学研究院)有限公司,江苏 南京 211006;
2. 国网电力科学研究院武汉南瑞有限责任公司,湖北 武汉430074;3. 武汉大学 电气与自动化学院,湖北 武汉 430072
Author(s):
XU Xiaolu12ZHOU Wen12ZHOU Dongguo3ZHU Shiqin12NI Hui12LUO Chuanxian12
1.NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing 211006, China; 2. Wuhan NARI Limited Liability Company of State Grid Electric Power Research Institute, Wuhan 430074, China; 3. Wuhan University, School of Electrical Engineering and Automation, Wuhan 430072, China
关键词:
脉冲耦合神经网络电力设备故障红外图像分层聚类区域边界
Keywords:
PCNN electronical equipment failure infrared image hierarchical clustering region boundary
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.202008017
文献标志码:
A
摘要:
为了在电力设备红外图像中较好地检测故障区域,提出一种基于分层聚类迭代的红外图像故障区域自动提取方法。在该方法中,首先以脉冲耦合神经网络(Pulse-coupled neural network,PCNN)作为红外图像处理核心模型,通过设置PCNN模型内在参数以及引入聚类机理,使得模型在迭代过程中可将整个图像划分成多个具有相似特性的区域。在此基础上,通过计算各个层点火区域均值以及对均值大小进行排序,然后针对灰度值较高的点火区域,结合边界检测算子并利用相似度评价方式对相邻区域进行合并处理,实现红外图像中热故障区域的有效提取。最后对真实红外图像进行测试并对比现有的一些方法,验证文中方法对热故障区域提取的有效性和适用性。
Abstract:
This paper proposes a method of extracting fault regions from infrared images of electronic equipment using a pulse-coupled neural network (PCNN); the method is based on iterative clustering. The PCNN model is used as the kernel method for image processing. Several parameters are first determined, and then hierarchical clustering is introduced to enable the PCNN model to segment an image into multiple regions on the basis of the inner similarity. In addition, the cluster centers are computed and sorted from large to small to find the pulse region with the highest brightness. The merging processing is final carried out with its neighboring region and the measure of similarity. The method thus improves the ability of the PCNN model to segment infrared images efficiently and effectively identifies thermal fault regions. Experimental results show that the proposed method exhibits good segmentation performance and is suitable for processing infrared images of thermal fault regions.

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备注/Memo

备注/Memo:
收稿日期:2019-12-13;修订日期:2020-07-01.
作者简介:许晓路(1988-),男,湖北荆门人,工程师,研究方向为输变电设备远程运维及评价诊断。E-mail:505787574@qq.com。
通信作者:周东国(1985-),男,浙江上虞人,讲师,博士,主要研究方向为红外图像处理、模式识别及电力信息处理等方向。E-mail:dgzhou1985@whu.edu.cn。
基金项目:国家电网公司总部科技项目资助,基于多源异构数据融合的特高压变电设备远程诊断关键技术研究及应用(GY71-17-012)。
更新日期/Last Update: 2020-08-20