Power electric transformer fault diagnosis based on infrared thermal images using wasserstein generative adversarial networks and deep learning classifier

Electronics (Switzerland)

Volumn 10, Issue 10, 2021, Pages

  Fanchiang, Kuo Hao ^a   Huang, Yen Chih ^a   Kuo, Cheng Chien ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Convolutional neural networks; Fault diagnosis; Generative adversarial networks; Image reconstruction; Infrared thermography; Transformers

Indexed keywords

EID: 85105721748     PISSN: None     EISSN: 20799292     Source Type: Journal    
DOI: 10.3390/electronics10101161     Document Type: Article

References (51)

1. Chen, P.; Huang, Y.; Zeng, F.; Jin, Y.; Zhao, X.; Wang, J. Review on insulation and reliability of dry-type transformer. In Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, 20–24 November 2019.
2. Mafra, R.; Magalhães, E.; Anselmo, B.; Belchior, F.; Lima e Silva, S.M.M. Winding hottest-spot temperature analysis in dry-type transformer using numerical simulation. Energies 2018, 12, 68. [CrossRef]
3. Duan, X.; Zhao, T.; Liu, J.; Zhang, L.; Zou, L. Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method. Energies 2018, 11, 2404. [CrossRef]
4. Senobari, R.K.; Sadeh, J.; Borsi, H. Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review. Electric. Power Syst. Res. 2018, 155, 172–183. [CrossRef]
5. Zhang, Z.; Gao, W.; Kari, T.; Lin, H. Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier. Energies 2018, 11, 2434. [CrossRef]
6. Li, E.; Wang, L.; Song, B.; Jian, S. Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data. Energies 2018, 11, 2344. [CrossRef]
7. Bagheri, M.; Zollanvari, A.; Nezhivenko, S. Transformer fault condition prognosis using vibration signals over cloud environment. IEEE Access 2018, 6, 9862–9874. [CrossRef]
8. Sun, Y.; Hua, Y.; Wang, E.; Li, N.; Ma, S.; Zhang, L.; Hu, Y. A temperature-based fault pre-warning method for the dry-type transformer in the offshore oil platform. Int. J. Electr. Power Energy Syst. 2020, 123, 106218. [CrossRef]
9. Chen, M.-K.; Chen, J.-M.; Cheng, C.-Y. Partial discharge detection in 11.4 kV cast resin power transformer. IEEE Trans. Dielectr. Electr. Insul. 2016, 23, 2223–2231. [CrossRef]
10. Athikessavan, S.C.; Jeyasankar, E.; Manohar, S.S.; Panda, S.K. Inter-turn fault detection of dry-type transformers using core-leakage fluxes. IEEE Trans. Power Deliv. 2019, 34, 1230–1241. [CrossRef]
11. Gockenbach, E.; Werle, P.; Borsi, H. Monitoring and diagnostic systems for dry type transformers. In Proceedings of the ICSD’01 2001 IEEE 7th International Conference on Solid Dielectrics (Cat. No.01CH37117), Eindhoven, The Netherlands, 25–29 June 2001. [CrossRef]
12. Lee, C.-T.; Horng, S.-C. Abnormality detection of cast-resin transformers using the fuzzy logic clustering decision tree. Energies 2020, 13, 2546. [CrossRef]
13. Tang, S.; Hale, C.; Thaker, H. Reliability modeling of power transformers with maintenance outage. Syst. Sci. Control Eng. 2014, 2, 316–324. [CrossRef]
14. Tenbohlen, S.; Vahidi, F.; Jagers, J. A Worldwide Transformer Reliability Survey. In Proceedings of the VDE High Voltage Technology 2016, ETG-Symposium, Berlin, Germany, 14–16 November 2016; pp. 1–6.
15. Murugan, R.; Ramasamy, R. Understanding the power transformer component failures for health index-based maintenance planning in electric utilities. Eng. Fail. Anal. 2019, 96, 274–288. [CrossRef]
16. Alonso, P.E.B.; Meana-Fernández, A.; Oro, J.M.F. Thermal response and failure mode evaluation of a dry-type transformer. Appl. Therm. Eng. 2017, 120, 763–771. [CrossRef]
17. Osornio-Rios, R.A.; Antonino-Daviu, J.A.; de Jesus Romero-Troncoso, R. Recent Industrial Applications of Infrared Thermography: A Review. IEEE Trans. Ind. Inform. 2019, 15, 615–625. [CrossRef]
18. Zou, H.; Huang, F. A novel intelligent fault diagnosis method for electrical equipment using infrared thermography. Infrared Phys. Technol. 2015, 73, 29–35. [CrossRef]
19. López-Pérez, D.; Antonino-Daviu, J. Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories. IEEE Trans. Ind. Appl. 2017, 53, 1901–1908. [CrossRef]
20. Duan, J.; He, Y.; Du, B.; Ghandour, R.M.R.; Wu, W.; Zhang, H. Intelligent Localization of Transformer Internal Degradations Combining Deep Convolutional Neural Networks and Image Segmentation. IEEE Access 2019, 7, 62705–62720. [CrossRef]
21. Janssens, O.; Loccufier, M.; van Hoecke, S. Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery. IEEE Trans. Ind. Inform. 2019, 15, 434–444. [CrossRef]
22. Siddiqui, Z.A.; Park, U.; Lee, S.-W.; Jung, N.-J.; Choi, M.; Lim, C.; Seo, J.-H. Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network. Sensors 2018, 18, 3837. [CrossRef]
23. Matuszewski, J.; Pietrow, D. Recognition of electromagnetic sources with the use of deep neural networks. In Proceedings of the XII Conference on Reconnaissance and Electronic Warfare Systems, Oltarzew, Poland, 19–21 November 2018.
24. Ma, S.; Cai, W.; Liu, W.; Shang, Z.; Liu, G. A lighted deep convolutional neural network based fault diagnosis of rotating machinery. Sensors 2019, 19, 2381. [CrossRef]
25. Kang, Q.; Zhao, H.; Yang, D.; Ahmed, H.S.; Ma, J. Lightweight convolutional neural network for vehicle recognition in thermal infrared images. Infrared Phys. Technol. 2020, 104, 103120. [CrossRef]
26. Iandola, F.N.; Han, S.; Moskewicz, M.W.; Ashraf, K.; Dally, W.J.; Keutzer, K. Squeezenet: Alexnet-level accuracy with 50× fewer parameters and<1mb model size, CoRR. arXiv 2016, arXiv:1602.07360.
27. Biswas, D.; Su, H.; Wang, C.; Stevanovic, A.; Wang, W. An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD. Phys. Chem. Earth 2019, 110, 176–184. [CrossRef]
28. Zhang, X.; Zhou, X.; Lin, M.; Sun, J. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018. [CrossRef]
29. Masci, J.; Meier, U.; Ciresan, D.; Schmidhuber, J. Stacked convolutional auto-encoders for hierarchical feature extraction. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2011; pp. 52–59.
30. Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 8–13 December 2014; pp. 2672–2680.
31. Wiatrak, M.; Albrecht, S.V.; Nystrom, A. Stabilizing Generative Adversarial Networks: A Survey. arXiv 2020, arXiv:1910.00927v2. Available online: https://arxiv.org/pdf/1910.00927.pdf (accessed on 12 May 2021).
32. Wang, Z.; She, Q.; Ward, T.E. Generative adversarial networks in computer vision: A survey and taxonomy. ACM Comput. Surv. 2021, 54, 1–38.
33. Arjovsky, M.; Bottou, L. Towards Principled Methods for Training Generative adversarial Networks. arXiv 2017, arXiv:1701.04862. Available online: https://arxiv.org/abs/1701.04862 (accessed on 12 May 2021).
34. Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; Chen, X. Improved techniques for training GANs. In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 5–10 December 2016; pp. 2234–2242.
35. Arjovsky, M.; Chintala, S.; Bottou, L. Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6–11 August 2017; Volume 70, pp. 214–223.
36. Akcay, S.; Abarghouei, A.A.; Breckon, T.P. GANomaly: Semi-supervised Anomaly Detection via Adversarial Training. ACCV 2018, 11363, 622–637.
37. Wang, Z.; Chen, J.; Hoi, S.C.H. Deep learning for image super-resolution: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 2020, 1. [CrossRef]
38. Borji, A. Pros and cons of GAN evaluation measures. Comput. Vis. Image Underst. 2019, 179, 41–65. [CrossRef]
39. Han, J.; Moraga, C. The influence of the sigmoid function parameters on the speed of backpropagation learning. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 1995; pp. 195–201.
40. Agarap, A.F. Deep Learning Using Rectified Linear Units (ReLU). arXiv 2018, arXiv:1803.08375.
41. Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017.
42. Yao, D.; Liu, H.; Yang, J.; Li, X. A lightweight neural network with strong robustness for bearing fault diagnosis. Measurement 2020, 159, 107756. [CrossRef]
43. Gong, W.; Chen, H.; Zhang, Z.; Zhang, M.; Wang, R.; Guan, C.; Wang, Q. A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors 2019, 19, 1693. [CrossRef] [PubMed]
44. Isola, P.; Zhu, J.-Y.; Zhou, T.; Efros, A.A. Image-to-image translation with conditional adversarial networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017.
45. Duchi, J.C.; Hazan, E.; Singer, Y. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res. 2011, 12, 2121–2159.
46. Qian, N. On the momentum term in gradient descent learning algorithms. Neural Netw. 1999, 12, 145–151. [CrossRef]
47. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90. [CrossRef]
48. LeCun, Y. LeNet-5, Convolutional Neural Networks. 2015. Available online: http://yann.lecun.com/exdb/lenet (accessed on 12 May 2021).
49. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NE, USA, 26 June–1 July 2016.
50. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556v6.
51. He, H.; Garcia, E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 2009, 21, 1263–1284.