HUANG Zhihong, WU Sheng, XIAO Jian, ZHANG Keren, HUANG Wei. Thermal Fault Diagnosis of Power Equipments Based on Guided Filter[J]. Infrared Technology , 2021, 43(9): 910-915.
Citation: HUANG Zhihong, WU Sheng, XIAO Jian, ZHANG Keren, HUANG Wei. Thermal Fault Diagnosis of Power Equipments Based on Guided Filter[J]. Infrared Technology , 2021, 43(9): 910-915.

Thermal Fault Diagnosis of Power Equipments Based on Guided Filter

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  • Received Date: December 29, 2020
  • Revised Date: March 10, 2021
  • Thermal fault is a common fault type in the power equipment. This paper introduces a thermal fault diagnosis method for the power equipment by employing guided filter. The proposed method consists of two main steps. First, according to the temperature difference between the thermal fault area and the background in infrared images, the Mahalanobis distance between the fault area and the background is estimated, and the initial thermal fault diagnosis results can be generated. Then, guided filter is used to improve the diagnosis performance, by utilizing the spatial correlation between adjacent pixels fully utilized. Experimental results demonstrate that the proposed diagnosis method has a better diagnosis performance than the current state-of-the-art detectors.
  • [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): 1198-1202. http://hwjs.nvir.cn/article/id/a00b6f68-052d-40c0-a00f-1f0ff120ce69

    LIU Rong, LIU Hui, JIA Ran, et al. Design of intelligent infrared diagnosis 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]
    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 newtechnologies and applications for live work[J]. Hunan Electric Power, 2018, 38(5): 1-4. DOI: 10.3969/j.issn.1008-0198.2018.05.001
    [4]
    康龙. 基于红外图像处理的变电站设备故障诊断[D]. 北京: 华北电力大学, 2016.

    KANG Long. Fault diagnosis of substation equipment based on infrared image processing[D]. Beijing: North China Electric Power University, 2016.
    [5]
    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
    [6]
    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
    [7]
    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 experimental study[J]. Electrical Technology, 2013, 14(6): 75-78. DOI: 10.3969/j.issn.1673-3800.2013.06.020
    [8]
    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, 2015, 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
    [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. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 2016, 42(9): 1300-1312. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201609002.htm
    [10]
    贾鑫. 基于双监督信号卷积神经网络的电气设备红外故障识别研究[D]. 天津: 天津理工大学, 2018.

    JIA Xin. Research on Infrared Fault Identification of Electrical Equipment Based on Double Supervised Signal Convolution Neural Network[D]. Tianjin: Tianjin University of Technology, 2018.
    [11]
    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
    [12]
    KANG Xudong, ZHANG Xiangping, LI Shutao, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5600-5611. DOI: 10.1109/TGRS.2017.2710145
    [13]
    HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering[J] IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. DOI: 10.1109/TPAMI.2012.213
    [14]
    Durand F, Dorsey J. Fast bilateral filtering for the display of high - dynamic-range images[J]. ACM Transactions on Graphics, 2002, 21(3): 257-266. DOI: 10.1145/566654.566574
    [15]
    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
    [16]
    ZHANG Yanfei, DU Bo, ZHANG Liangping, et al. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1376-1389. DOI: 10.1109/TGRS.2015.2479299
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