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An Improved U-Net Segmentation Model That Integrates a Dual Attention Mechanism and a Residual Network for Transformer Oil Leakage Detection

Author

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  • Xuxu Li

    (State Grid Sichuan Electric Power Company, Chengdu 610041, China)

  • Xiaojiang Liu

    (State Grid Sichuan Electric Power Institute, Chengdu 610041, China)

  • Yun Xiao

    (State Grid Sichuan Electric Power Company, Chengdu 610041, China)

  • Yao Zhang

    (State Grid Sichuan Ultra High Voltage Company, Chengdu 610041, China)

  • Xiaomei Yang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Wenhai Zhang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

Accurately detecting oil leakage from a power transformer is important to maintain its normal operation. Deep learning (DL) methods have achieved satisfactory performance in automatic oil detection, but challenges remain due to the small amount of training data and oil targets with large variations in position, shape, and scale. To manage these issues, we propose a dual attention residual U-net (DAttRes-Unet) within a U-net architecture that extensively uses a residual network as well as spatial and channel-wise attention modules. To overcome the vanishing gradient problem due to deeper layers and a small amount of training data, a residual module from ResNet18 is used to construct the encoder path in the U-net framework. Meanwhile, to overcome the issue of training difficulty for the network, inspired by the advantage of transfer learning, initial network parameters in the encoder are obtained from the pre-trained ResNet18 on the ImageNet dataset. Further, in the decoder path, spatial attention and channel attention are integrated to highlight oil-stained regions while suppressing the background or irrelevant parts/channels. To facilitate the acquisition of the fluorescence images of the transformer, we designed a portable acquisition device integrating an ultraviolet light source and a digital camera. The proposed network is trained on the amount of fluorescence images after data augmentation is used and tested on actual fluorescence images. The experiment results show that the proposed DAttRes-Unet network can recognize oil-stained regions with a high accuracy of 98.49 % for various shapes and scales of oil leakage.

Suggested Citation

  • Xuxu Li & Xiaojiang Liu & Yun Xiao & Yao Zhang & Xiaomei Yang & Wenhai Zhang, 2022. "An Improved U-Net Segmentation Model That Integrates a Dual Attention Mechanism and a Residual Network for Transformer Oil Leakage Detection," Energies, MDPI, vol. 15(12), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4238-:d:834644
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    References listed on IDEAS

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    1. Parisa Asadi & Lauren E. Beckingham, 2021. "Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images," Energies, MDPI, vol. 14(15), pages 1-21, July.
    2. Stefan Hensel & Marin B. Marinov & Michael Koch & Dimitar Arnaudov, 2021. "Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation," Energies, MDPI, vol. 14(19), pages 1-14, September.
    3. Mohammad Junaid & Zsolt Szalay & Árpád Török, 2021. "Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions," Energies, MDPI, vol. 14(21), pages 1-16, November.
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