Statistical analysis of infrared thermogram for CNN-based electrical equipment identification methods

References

• [dataset] Yetgin, Ö. 2017. Powerline image dataset (Infrared-IR and Visible Light-VL). Mendeley Data v7. doi:10.17632/n6wrv4ry6v.7.
   Google Scholar
• Administration of National Energy, China, 2016. Application rules of infrared diagnosis for live electrical equipment.
   Google Scholar
• Alfredo Osornio-Rios, R., J. A. Antonino-Daviu, and R. de Jesus Romero-troncoso. 2019. Recent industrial applications of infrared thermography: A review. IEEE Trans Ind Inform 15:350–368. doi:10.1109/TII.2018.2884738.
   Web of Science ®Google Scholar
• Bahnsen, C. H., and T. B. Moeslund. 2019. Rain removal in traffic surveillance: Does it matter? IEEE Trans Intell Transp 20 (2802–2819). doi: 10.1109/TITS.2018.2872502.
   PubMedGoogle Scholar
• Gong, X., Q. Yao, M. Wang, and Y. Lin. 2018. A deep learning approach for oriented electrical equipment detection in thermal images. IEEE Access 6:41590–97. doi:10.1109/ACCESS.2018.2859048.
   Web of Science ®Google Scholar
• Hall-Beyer, M., 2017. GLCM texture: a tutorial v. 3.0. http://www.ucalgary.ca/UofC/nasdev/mhallbey/research.htm
   Google Scholar
• Han, S., F. Yang, G. Yang, B. Gao, N. Zhang, and D. Wang. 2020. Electrical equipment identification in infrared images based on ROI-selected CNN method. Electr Pow Syst Res 188:106534. doi:10.1016/j.epsr.2020.106534.
   Web of Science ®Google Scholar
• Howard, A. G., and Menglong, Z. et al, MobileNets: Efficient convolutional neural networks for mobile vision applications. https://arxiv.org/abs/1704.04861
   Google Scholar
• Huda, A. S. N., and S. Taib. 2013. Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography. Infrared Phys Techn 61:184–91. doi:10.1016/j.infrared.2013.04.012.
   Web of Science ®Google Scholar
• Jiang, J., Y. Bie, J. Li, X. Yang, G. Ma, Y. Lu, and C. Zhang. 2020. Fault diagnosis of the bushing infrared images based on mask R-CNN and improved PCNN joint algorithm. High Voltage. doi:10.1049/hve.2019.0249.
   Web of Science ®Google Scholar
• Liu, Y., X. Ji, S. Pei, Z. Ma, G. Zhang, Y. Lin, and Y. Chen. 2020. Research on automatic location and recognition of insulators in substation based on YOLOv3. High Voltage 5 (1):62–68. doi:10.1049/hve.2019.0091.
   Web of Science ®Google Scholar
• López-Pérez, J. A. A. D., 2017. Failure detection in industrial electric motors through the use of infrared-based isothermal representation., Proc. 43rd Annu. Conf. IEEE Ind. Electron. Soc., Beijing, China, pp. 3822–27.
   Google Scholar
• Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang, 2017. The Expressive Power of Neural Networks: A View from the Width.
   Google Scholar
• Palmero, C., A. Clapés, C. Bahnsen, A. Møgelmose, T. B. Moeslund, and S. Escalera. 2016. Multi-modal RGB–Depth–Thermal human body segmentation. Int J Comput Vision 118 (2):217–39. doi:10.1007/s11263-016-0901-x.
   Web of Science ®Google Scholar
• Ramachandran, V. S. 1988. Perception of shape from shading. Nature (6152):163. doi:10.1038/331163a0.
   PubMed Web of Science ®Google Scholar
• Sanchez, A. M. A. B. 2016. Analyzing the influence of contrast in large-scale recognition of natural images. Integr Comput-Aid E 3 (3):221–35. doi:10.3233/ICA-160516.
   Google Scholar
• Silva, B. P. A., R. A. M. Ferreira, S. C. Gomes, F. A. R. Calado, R. M. Andrade, and M. P. Porto. 2018. On-rail solution for autonomous inspections in electrical substations. Infrared Phys Techn 90:53–58. doi:10.1016/j.infrared.2018.01.019.
   Web of Science ®Google Scholar
• Ullah, I., R. U. Khan, F. Yang, and L. Wuttisittikulkij. 2020. Deep learning image-based defect detection in high voltage electrical equipment. Energies (Basel) 13 (2):392. doi:10.3390/en13020392.
   Web of Science ®Google Scholar
• Usamentiaga, R., M. A. Fernandez, A. F. Villan, and J. L. Carus. 2018. Temperature monitoring for electrical substations using infrared thermography: Architecture for industrial internet of things. IEEE Trans Ind Inform 14 (12):5667–77. doi:10.1109/TII.2018.2868452.
   Web of Science ®Google Scholar
• Wang, F., L. Chen, C. Li, S. Huang, Y. Chen, C. Qian, and C. C. Loy, 2018. The Devil of Face Recognition is in the Noise.
   Google Scholar
• Zhao, Z., X. Fan, G. Xu, L. Zhang, Y. Qi, and K. Zhang. 2017. Aggregating deep convolutional feature maps for insulator detection in infrared images. IEEE Access 5:21831–39. doi:10.1109/ACCESS.2017.2757030.
   Web of Science ®Google Scholar
• Zou, H., and F. Huang. 2015. A novel intelligent fault diagnosis method for electrical equipment using infrared thermography. Infrared Phys Techn 73:29–35. doi:10.1016/j.infrared.2015.08.019.
   Web of Science ®Google Scholar