Advanced Search
中文
Volume 45 Issue 6
Jun.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
DONG Yifei, WANG Xiaojie, WANG Renshu, XU Jun, SHU Shengwen, TAO Yiqing. Thermal Defect Detection of Composite Insulator Based on One-dimensional Residual Network[J]. Infrared Technology , 2023, 45(6): 663-670.
Citation: DONG Yifei, WANG Xiaojie, WANG Renshu, XU Jun, SHU Shengwen, TAO Yiqing. Thermal Defect Detection of Composite Insulator Based on One-dimensional Residual Network[J]. Infrared Technology , 2023, 45(6): 663-670.
shu

Thermal Defect Detection of Composite Insulator Based on One-dimensional Residual Network

  • 1.

    School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

  • 2.

    Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007, China

More Information
  • Received Date: November 07, 2021
  • Revised Date: December 29, 2021
    Graphical Abstract
    • Abstract
  • A composite insulator exhibits different heating characteristics under different defect types. In this study, a thermal defect detection method for composite insulators based on one-dimensional residual network and the central axis temperature data of composite insulators is proposed. First, the abnormal temperature rise range and position information of composite insulators under different defect types were statistically analyzed, and a sample set of composite insulator central axis temperature data under different defect types was obtained. Next, a one-dimensional residual network model was established. The dilated convolution was introduced into the residual block to expand the receptive field, and the efficient channel attention network (ECA_Net) was added to improve the feature weight with a high correlation with the defect category. Finally, numerical examples were verified and compared. Simultaneously, the t-distributed stochastic neighbor embedding (t-SNE) visualization method was used to reflect the effect of feature extraction on the model. The results showed that the model effectively captured the spatial dimension information of the central axis temperature data and adaptively extracted the features with high classification discrimination. Compared with ordinary convolution, auto encoder (AE), and support vector machine(SVM), the proposed model has improved recognition accuracy, good robustness and generalization ability. Thus, end-to-end composite insulator heating defect detection was realized.
    • FullText(HTML)
    • References (18)
  • [1]
    关志成, 刘瑛岩, 周远翔, 等. 绝缘子及输电设备外绝缘[M]. 北京: 清华大学出版社, 2006: 66-67.

    GUAN Zhicheng, LIU Yingyan, ZHOU Yuanxiang, et al. Insulators and External Insulation of the Transmission and Distribution Equipment[M]. Beijing: Tsinghua University Press, 2006: 66-67.
    [2]
    律方成, 刘杰, 石鹏, 等. 复合绝缘子故障检测方法的有效性分析[J]. 高压电器, 2013, 49(5): 95-100. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ201305020.htm

    LV Fangcheng, LIU Jie, SHI Peng, et al. Effectiveness analysis of composite insulator fault detection method[J]. High Voltage Apparatus, 2013, 49(5): 95-100. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ201305020.htm
    [3]
    何洪英. 基于红外热像及人工智能的绝缘子污秽等级识别方法研究[D]. 长沙: 湖南大学, 2006.

    HE Hongying. Research on Insulator Pollution Level Identification Method Based on Infrared Thermal Image and Artificial Intelligence[D]. Changsha: Hunan University, 2006.
    [4]
    陈艺, 张先捷, 欧阳宗帅, 等. 基于主成分分析的复合绝缘子运行状态综合评价方法研究[J]. 电瓷避雷器, 2020(6): 176-186. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ202006027.htm

    CHEN Yi, ZHANG Xianjie, OUYANG Zongshuai, et al. Study on comprehensive evaluation method of composite insulator operation state based on principal component analysis[J]. Insulators and Surge Arresters, 2020(6): 176-186. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ202006027.htm
    [5]
    杨政勃, 金立军, 张文豪, 等. 基于红外图像识别的输电线路故障诊断[J]. 现代电力, 2012, 29(2): 76-79. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDL201202018.htm

    YANG Zhengbo, JIN Lijun, ZHANG Wenhao, et al. Transmission line fault diagnosis based on infrared image recognition[J]. Modern Power, 2012, 29(2): 76-79. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDL201202018.htm
    [6]
    张美金, 屈秋帛. 基于GWO-SVM的红外热成像低零值绝缘子识别[J]. 红外技术, 2021, 43(4): 397-402. http://hwjs.nvir.cn/article/id/2e60cc20-25fd-4344-8e38-ff3768e03fa2

    ZHANG Meijin, QU Qiubo. Infrared thermal imaging low zero value insulator identification based on GWO-SVM[J]. Infrared Technology, 2021, 43(4): 397-402. http://hwjs.nvir.cn/article/id/2e60cc20-25fd-4344-8e38-ff3768e03fa2
    [7]
    陈金法. 绝缘子红外热像检测及诊断技术研究[D]. 杭州: 浙江大学, 2011.

    CHEN Jinfa. Research on Insulator Infrared Thermography Detection and Diagnosis Technology[D]. Hangzhou: Zhejiang University, 2011.
    [8]
    陈林聪, 肖雄, 王建国, 等. 基于红外热像轴线温度识别的复合绝缘子界面缺陷鉴别方法: 201711079351.6[P]. 2017-11-06.

    CHEN Lincong, XIAO Xiong, WANG Jianguo, et al. Identification method of interface defects of composite insulators based on infrared thermal image axis temperature identification: 201711079351.6[P]. 2017-11-06.
    [9]
    王维博, 张斌, 曾文入, 等. 基于特征融合一维卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2020, 48(6): 53-60. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202006007.htm

    WANG Weibo, ZHANG Bin, ZENG Wenru, et al. Power quality disturbance classification based on feature fusion one-dimensional convolutional neural network[J]. Power System Protection and Control, 2020, 48(6): 53-60. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202006007.htm
    [10]
    HAO R F, LIU C, CHENG Y Q. Application of CNN-1d based on feature fusion in bearing fault diagnosis[C]//2020 Eighth International Conference on Advanced Cloud and Big Data(CBD), 2020: 195-200.
    [11]
    WU X Y, PENG Z K, REN J S, et al. Rub-impact fault diagnosis of rotating machinery based on 1-D convolutional neural networks[J]. IEEE Sensors Journal, 2020, 20(15): 8349-8363.
    [12]
    金列俊, 詹建明, 陈俊华, 等. 基于一维卷积神经网络的钻杆故障诊断[J]. 浙江大学学报(工学版), 2020, 54(3): 467-474. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202003007.htm

    JIN Liejun, ZHAN Jianming, CHEN Junhua, et al. Drill pipe fault diagnosis based on one-dimensional convolutional neural network[J]. Journal of Zhejiang University(Engineering Edition), 2020, 54(3): 467-474. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202003007.htm
    [13]
    CHENG Z P, GUO K X, WU C F, et al. U-Net cascaded with dilated convolution for medical image registration[C]//2019 Chinese Automation Congress (CAC), 2019: 3647-3651.
    [14]
    ZHANG X, ZOU Y, SHI W. Dilated convolution neural network with leaky ReLU for environmental sound classification[C]//2017 22nd International Conference on Digital Signal Processing (DSP), 2017: 1-5.
    [15]
    ZHANG K, SUN M, HAN T X, et al. Residual networks of residual networks: Multilevel residual networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(6): 1303-1314.
    [16]
    韩兴, 张红英, 张媛媛. 基于高效通道注意力网络的人脸表情识别[J]. 传感器与微系统, 2021, 40(1): 118-121. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202101034.htm

    HAN Xing, ZHANG Hongying, ZHANG Yuanyuan. Facial expression recognition based on efficient channel attention network[J]. Transducer and Microsystems Technologies, 2021, 40(1): 118-121. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202101034.htm
    [17]
    MA X Y, LI D, ZHANG J Y, et al. T-SNE with high order truncation fractional gradient descent method[C]//2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference(ITAIC), 2020: 1929-1933.
    [18]
    Gisbrecht A, Schulz A, Hammer B. Parametric nonlinear dimensionality reduction using kernel t-SNE[J]. Neurocomputing, 2015, 147: 71-82.
    • Related Articles

  • Related Articles

    [1]LIAO Guangfeng, GUAN Zhiwei, CHEN Qiang. An Improved Dual Discriminator Generative Adversarial Network Algorithm for Infrared and Visible Image Fusion[J]. Infrared Technology , 2025, 47(3): 367-375.
    [2]DAI Yueming, YANG Lufeng, TONG Xiongmin. Real-time Section State Verification Method of Energy Management System Low Voltage Equipment Based on Infrared Image and Deep Learning[J]. Infrared Technology , 2024, 46(12): 1464-1470.
    [3]CHEN Haipeng, JIN Weiqi, LI Li, QIU Su, YU Xiangzhi. Study on BRDF Scattering Characteristics of Relay Wall in Non-Line-of-Sight Imaging Based on Time-gated SPAD Array[J]. Infrared Technology , 2024, 46(11): 1225-1234.
    [4]ZHONG Guoli, LIAO Shouyi, YANG Xinjie. Real-Time Infrared Image Generation of Battlefield Environment Based on JRM[J]. Infrared Technology , 2024, 46(2): 183-189.
    [5]SHEN Ji, NA Qiyue, XU Jiandong, CHANG Weijing, ZHANG Wei, JIAN Yunfei. 640×512 Frame Transfer EMCCD Camera Timing Sequence Design[J]. Infrared Technology , 2023, 45(5): 548-552.
    [6]WANG Mingxing, ZHENG Fu, WANG Yanqiu, SUN Zhibin. Time-of-Flight Point Cloud Denoising Method Based on Confidence Level[J]. Infrared Technology , 2022, 44(5): 513-520.
    [7]LIU Zhaoqing, LI Li, DONG Bing, JIN Weiqi. Shack-Hartman Detector Real-time Wavefront Processor Based on FPGA[J]. Infrared Technology , 2021, 43(8): 717-722.
    [8]CHEN Zheng, FU Kuisheng, DING Haishan. Analysis of the Influence of Installation Errors of an Infrared Stabilized Platform on Line-of-sight Angular Velocity[J]. Infrared Technology , 2021, 43(2): 110-115.
    [9]WEI Jiali, QU Huidong, WANG Yongxian, ZHU Junqing, GUAN Yingjun. Research Review of 3D Cameras Based on Time-of-Flight Method[J]. Infrared Technology , 2021, 43(1): 60-67.
    [10]HUANG Minshuang, GUAN Zaihui, JIANG Bo. Pulse Laser Ranging Using Sinusoidal Amplitude Time Conversion[J]. Infrared Technology , 2020, 42(5): 483-487.

  • Cited by

    Periodical cited type(0)

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views (123) PDF downloads (23) Cited by(3)
    Related

    Copyright © 《Infrared Technology》Editorial Office京ICP备000000000号-1

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return