Infrared and Laser Engineering, Volume. 47, Issue 7, 703003(2018)

Infrared faults recognition for electrical equipments based on dual supervision signals deep learning

Jia Xin1...2, Zhang Jinglei1,2 and Wen Xianbin3 |Show fewer author(s)
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  • 1[in Chinese]
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    References(10)

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    [2] [2] Wei Gang, Feng Zhongzheng, Tang Yue, et al. The infrared diagnostic technology of power transmission devices and experimental study[J]. Electrical Engineering, 2013, 14(6): 75-78. (in Chinese)

    [3] [3] Wang Jialin, Cui Haoyang, Xu Yong, et al. Infrared image diagnosis method of transformer substation equipment base on SOM neural network[J]. Journal of Shanghai University of Electric Power, 2016, 32(1): 78-82. (in Chinese)

    [4] [4] Zhang Difei, Zhang Jinsuo, Yao Keming, et al. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 0104001. (in Chinese)

    [5] [5] Xie Saining, Ross Girshick, Piotr Dollár, et al. Aggregated residual transformations for deep neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1492-1500.

    [6] [6] Wang Wanguo, Tian Bing, Liu Yue, et al. Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J]. Journal of Geo-Information Science, 2017, 19(2): 256-263. (in Chinese)

    [7] [7] Liu Bin, Zhang Jian. Partial discharge recogniton in power transformers based on convolutional neural networks[J]. High Voltage Apparatus, 2017, 53(5): 70-74. (in Chinese)

    [8] [8] Wang Juan, Wang Ping, Wang Gang. Stippled direct part mark location based on self-adaptive super-pixels segmentation[J]. Acta Automatica Sinica, 2015, 41(5): 991-1003. (in Chinese)

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    [10] [10] Wen Yandong, Zhang Kaipeng, Li Zhifeng, et al. A discriminative feature learning approach for deep face recognition[C]//European Conference on Computer Vision, 2016: 499-515.

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    Jia Xin, Zhang Jinglei, Wen Xianbin. Infrared faults recognition for electrical equipments based on dual supervision signals deep learning[J]. Infrared and Laser Engineering, 2018, 47(7): 703003

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    Paper Information

    Category: 特约专栏—“深度学习及其应用”

    Received: Feb. 13, 2018

    Accepted: Mar. 17, 2018

    Published Online: Aug. 30, 2018

    The Author Email:

    DOI:10.3788/irla201847.0703003

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