Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments

HUANG Zhihong; HONG Feng; HUANG Wei

Volume 47 Issue 8

Aug. 2022

Turn off MathJax

Article Contents

Abstract

References

Infrared Technology > 2022 > 44(8): 870-874.

HUANG Zhihong, HONG Feng, HUANG Wei. Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments[J]. Infrared Technology , 2022, 44(8): 870-874.

Citation:

HUANG Zhihong, HONG Feng, HUANG Wei. Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments[J]. Infrared Technology , 2022, 44(8): 870-874.

Citation:

HUANG Zhihong, HONG Feng, HUANG Wei. Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments[J]. Infrared Technology , 2022, 44(8): 870-874.

PDF (848 KB)

Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments

1.
State Grid Hunan Electric Power Corporation Limited Research Institute, Changsha 410007, China
2.
State Grid Hunan Electric Power Corporation Limited, Changsha 410029, China
3.
Hunan Xiangdian Test and Research Institute Corporation Limited Research Institute 410007, China

More Information

Received Date: February 09, 2022
Revised Date: February 14, 2022

Graphical Abstract

Abstract

Abstract

This work introduces a thermal fault diagnosis method that integrates superpixel segmentation and low-rank representation for diagnosis. The proposed method comprises two main steps. First, an input infrared image is transformed using a principal component analysis (PCA) algorithm, and a superpixel segmentation method is employed for the first principal component (PC). The first PC is divided into non-overlapping homogeneous superpixels. Then, the thermal fault region is detected by employing low-rank representation in a superpixel-by-superpixel manner. Experimental results show that the proposed diagnosis method has a better detection performance than that of current state-of-the-art detectors.
- power equipment,
- infrared image,
- thermal fault diagnosis,
- superpixel segmentation,
- low-rank represent

FullText(HTML)

References (17)

References

[1]	刘嵘, 刘辉, 贾然, 等. 一种智能型电网设备红外诊断系统的设计[J]. 红外技术, 2020, 42(12): 198-1202. http://hwjs.nvir.cn/article/id/a00b6f68-052d-40c0-a00f-1f0ff120ce69 LIU Rong, LIU Hui, JIA Ran, et al. Design of intelligent infrared di-agnosis system for power grid equipment[J]. Infrared Technology, 2020, 42(12): 1198-1202. http://hwjs.nvir.cn/article/id/a00b6f68-052d-40c0-a00f-1f0ff120ce69
[2]	张文峰, 彭向阳, 陈锐民, 等. 基于无人机红外视频的输电线路发热缺陷智能诊断技术[J]. 电网技术, 2014, 38(5): 1334-1338. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201405034.htm ZHANG Wenfeng, PENG Xiangyang, CHEN Ruiming, et al. Intelligent diagnostic techniques of abnormal heat defect in transmission lines based on unmanned helicopter infrared video[J]. Power System Technology, 2014, 38(5): 1334-1338. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201405034.htm
[3]	王淼, 杜伟, 孙鸿博, 等. 基于红外图像识别的输电线路故障诊断方法[J]. 红外技术, 2017, 39(4): 383-386. http://hwjs.nvir.cn/article/id/hwjs201704015 WANG Miao, DU Wei, SUN Hongbo, et al. Transmission line fault diagnosis method based on infrared image recognition[J]. Infrared Technology, 2017, 39(4): 383-386. http://hwjs.nvir.cn/article/id/hwjs201704015
[4]	胡洛娜, 彭云竹, 石林鑫. 核猫群红外图像异常检测方法在电力智能巡检中的应用[J]. 红外技术, 2018, 40(9): 323-328. http://hwjs.nvir.cn/article/id/hwjs201809013 HU Luona, PENG Yunzhu, SHI Linxin. Anomaly detection method of infrared images based on kernel cat swarm optimization clustering with application in intelligent electrical power inspection[J]. Infrared Technology, 2018, 40(9): 323-328. http://hwjs.nvir.cn/article/id/hwjs201809013
[5]	魏钢, 冯中正, 唐跃林, 等. 输变电设备红外故障诊断技术与试验研究[J]. 电气技术, 2013, 14(6): 75-78. DOI: 10.3969/j.issn.1673-3800.2013.06.020 WEI Gang, FENG Zhongzheng, TANG Yuelin, et al. The infrared diagnostic technology of power transmission devices and experimen-tal study[J]. Electrical Technology, 2013, 14(6): 75-78. DOI: 10.3969/j.issn.1673-3800.2013.06.020
[6]	李鑫, 崔昊杨, 霍思佳, 等. 基于粒子群优化法的Niblack电力设备红外图像分割[J]. 红外技术, 2018, 40(8): 780-785. http://hwjs.nvir.cn/article/id/hwjs201808010 LI Xin, CUI Wuyang, HUO Siyang. Niblack's method for infrared image segmentation of electrical equipment improved by particle swarm optimization[J]. Infrared Technology, 2018, 40(8): 780-785. http://hwjs.nvir.cn/article/id/hwjs201808010
[7]	林颖, 郭志红, 陈玉峰. 基于卷积递归网络的电流互感器红外故障图像诊断[J]. 电力系统保护与控制, 2017, 45(16): 87-94. DOI: 10.7667/j.issn.1674-3415.2015.16.013 LIN Ying, GUO Zhihong, CHEN Yufeng. Convolutional-recursive network based current transformer infrared fault image diagnosis[J]. Power System Protection and Control, 2015, 45(16): 87-94. DOI: 10.7667/j.issn.1674-3415.2015.16.013
[8]	黄志鸿, 吴晟, 肖剑, 等. 基于引导滤波的电力设备热故障诊断方法研究[J]. 红外技术, 2021, 43(9): 910-915. http://hwjs.nvir.cn/article/id/cb2a71f1-cd7c-4e76-977b-b6f7472b905d HUANG Zhihong, WU Sheng, XIAO Jian, et al. Thermal fault dagnosis of power equipments based on guided filter[J]. Infrared Technology, 2021, 43(9): 910-915. http://hwjs.nvir.cn/article/id/cb2a71f1-cd7c-4e76-977b-b6f7472b905d
[9]	常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网[J]. 自动化学报, 2016, 42(9): 1300-1312. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201609002.htm CHANG Liang, DENG Xiaoming, ZHOU Mingquan, et al. Convolu-tional neural networks in image understanding[J]. Acta Automatica Sinica, 2016, 42(9): 1300-1312. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201609002.htm
[10]	魏东, 龚庆武, 来文青, 等. 基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J]. 中国电机工程学报, 2016, 36(5): 21-28. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC2016S1003.htm WEI Dong, LONG Qinwu, LAI Wenqing, et al. Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network[J]. Proceedings of the CSEE, 2016, 36(5): 21-28. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC2016S1003.htm
[11]	周可慧, 廖志伟, 肖异瑶, 等. 基于改进CNN的电力设备红外图像分类模型构建研究[J]. 红外技术, 2019, 41(11): 1033-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201911007.htm ZHOU Kehui, LIAO Zhiwei, XIAO Yiyao, et al. Construction of infrared image classification model for power equipments based on improved CNN[J]. Infrared Technology, 2019, 41(11): 1033-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201911007.htm
[12]	LIU M, Tuzel O, Ramalingam S, et al. Entropy rate superpixel segmentation[C]//Pattern Recognit., 2011: 2097-2104.
[13]	YUAN X, YANG J. Sparse and low-rank matrix decomposition via alternating direction methods[J]. Pacific. J. Optim, 1990, 9(1): 1760-1770.
[14]	Reed I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustic Speech Signal Processing, 1990, 38(10): 1760-1770. DOI: 10.1109/29.60107
[15]	KANG X, ZHANG X, LI S, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters[J]. IEEE Trans. Geosci. Remote Sens. , 2017, 55(10): 5600-5611. DOI: 10.1109/TGRS.2017.2710145
[16]	XU Y, WU Z, LI J, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Trans. Geosci Remote Sens. , 2016, 54(4): 1990 DOI: 10.1109/TGRS.2015.2493201
[17]	蒋昀宸, 樊绍胜, 陈骏星溆. 带电作业智能新技术及其应用现状[J]. 湖南电力, 2018, 38(5): 1-4. DOI: 10.3969/j.issn.1008-0198.2018.05.001 JIANG Yunchen, FAN Zhaosheng, CHEN Junxingxu. Smart new-technologies and applications for live work[J]. Hunan Electric Power, 2018, 38(5): 1-4. DOI: 10.3969/j.issn.1008-0198.2018.05.001

Cited By

Cited by

Periodical cited type(15)

1.	马战南，蒋俊英. 基于LMDI分解法的火电厂热力设备故障自动化诊断技术. 自动化与仪表. 2025(02): 89-92+98 .
2.	刘传洋，吴一全. 基于红外图像的电力设备识别及发热故障诊断方法研究进展. 中国电机工程学报. 2025(06): 2171-2196 .
3.	尤渺. 基于模糊Petri网和马尔科夫链理论的水电厂设备故障应急响应模型. 电子设计工程. 2024(10): 59-63 .
4.	刘沛津，张香瑞，魏平. 基于融合重构的电气设备红外图像EnFCM聚类分割方法. 红外技术. 2024(03): 295-304 . 本站查看
5.	金萍，侯娟. 面向新型电力系统的粗糙集和双流网络自动化物联设备故障诊断方法研究. 电测与仪表. 2024(09): 166-171 .
6.	张电，张凌跃，王宇，李伟，白困利 . 基于改进Apriori算法的电力物联设备故障安全筛选方法. 自动化与仪器仪表. 2024(09): 190-194 .
7.	房雪雷，马娟，徐结红，丁津津，彭勃. 基于数字孪生技术的电力设备故障诊断仿真系统构建研究. 自动化与仪器仪表. 2024(10): 233-236 .
8.	任正国，黄文琦，梁凌宇，郑桦. 基于高低频特征融合的电力设备热故障模糊检测方法. 自动化技术与应用. 2024(11): 39-42+55 .
9.	陶俊，郭庆，郭力旋，喻成琛. 融合声纹特征的智慧电网主设备故障自动化识别系统. 自动化与仪表. 2024(11): 106-110 .
10.	杨慢慢，尹兆磊，陈晨，白明辉. 线路多变条件下电力设备热稳定实时控制方法. 自动化与仪表. 2023(08): 26-30 .
11.	黄志鸿，肖剑，徐先勇，张辉. 基于谱残差变换的电力设备热缺陷识别技术. 红外技术. 2023(08): 884-889 . 本站查看
12.	刘明哲，毛振攀，周桐，李锦云，乔加奇. 基于LSTM神经网络的发电机绕组散热异常监测系统设计. 环境技术. 2023(07): 69-74 .
13.	陈凡，金东. 基于大数据的数字化电力设备故障诊断方法. 信息与电脑(理论版). 2023(15): 46-48 .
14.	杨明祥，李佳宣，殷商莹，贺皎，邹兰青. 基于改进关联规则的电力设备故障预测与诊断. 科学技术创新. 2023(27): 77-80 .
15.	梁剑，黄志鸿，张可人. 基于多尺度引导滤波和决策融合的电力设备热故障诊断方法研究. 红外技术. 2022(12): 1344-1350 . 本站查看

Other cited types(2)

Get Citation

PDF

XML

Article views (169) PDF downloads (32) Cited by(17)

Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments

Abstract

References

Cited by

Periodical cited type(15)

Other cited types(2)

Catalog

Related

Shape Adaptation Low Rank Representation for Thermal Fault Diagnosis of Power Equipments

Abstract

References

Cited by

Periodical cited type(15)

Other cited types(2)

Catalog

Related

Export File

Citation

Format

Content