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Fault detection in electrical equipment’s images by using optimal features with deep learning classifier

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Abstract

Infrared imaging frameworks have been broadly utilized as a part of the military and civil fields, for example, target recognition, fault diagnosis, fire identification, and medical analysis. Evaluating and monitoring the electrical parts is necessary to analyze the thermal fault at the beginning period. The paper presents the IRT electrical images for diagnosing and classifying the faults by the feature extraction and classification process. At first, IRT segmented switch image (highly temperature zone) is considered, followed by the feature extraction procedure is applied where the images are selected based on the optimal features. The optimal features are accomplished by the inspired optimization algorithm i.e. Opposition based Dragonfly Algorithm (ODA). It chose the best features for the unproblematic classification process. With the intention of classifying the segmented portion as faulty and non-faulty IRT, an approach Deep Neural Network (DNN) is presented. On the basis of the optimal weight attained from learning algorithm, categorize the faulty electrical image easily. The results show that the proposed work accomplishes maximum classification accuracy i.e. 99.99% compared to existing classification approaches.

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References

  1. Aujeszky T, Korres G, Eid M (2017) Thermography-based material classification using machine learning. In Haptic, Audio and Visual Environments and Games (HAVE), 2017 IEEE International Symposium on (pp. 1–6). IEEE

  2. Bagavathiappan S, Lahiri BB, Saravanan T, Philip J, Jayakumar T (2013) Infrared thermography for condition monitoring–A review. Infrared Phys Technol 60:35–55

    Article  Google Scholar 

  3. Chou YC, Yao L (2009) The automatic diagnostic system of electrical equipment using infrared thermography. In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of (pp. 155–160). IEEE

  4. Dashtizadeh Z, Ali A, Abdan K, Behmanesh M (2012) The use of infrared thermography in detecting the defects in kenaf-poly urethane composites. Polym-Plast Technol Eng 51(11):1155–1162

    Article  Google Scholar 

  5. ditLeksir YL, Mansour M, Moussaoui A (2017) Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine. Infrared Physics & Technology, 1–28

  6. Dutta, T., Sil, J. and Chottopadhyay, P., 2016. Condition monitoring of electrical equipment using thermal image processing. In Control, Measurement and Instrumentation (CMI), 2016 IEEE First International Conference on (pp. 311–315). IEEE.

  7. Glavaš H, Józsa L, Barić T (2016) Infrared thermography in an energy audit of electrical installations. Tehničkivjesnik 23(5):1533–1539

    Google Scholar 

  8. Glavaš H, Vukobratović M, Keser T (2018) Infrared thermography as control of handheld IPL device for home-use. Journal of Cosmetic and Laser Therapy, pp.1–9

  9. Glowacz A, Glowacz Z (2017) Diagnosis of the three-phase induction motor using thermal imaging. Infrared Phys Technol 81:7–16

    Article  Google Scholar 

  10. Huda ASN, Taib S (2013) Suitable features selection for monitoring the thermal condition of electrical equipment using infrared thermography. Infrared Phys Technol 61:184–191

    Article  Google Scholar 

  11. Huda AN, Taib S (2013) Application of infrared thermography for predictive/preventive maintenance of thermal defect in electrical equipment. Appl Therm Eng 61(2):220–227

    Article  Google Scholar 

  12. Hui Z, Fuzhen H (2015) An intelligent fault diagnosis method for electrical equipment using infrared images. In Control Conference (CCC), 2015 34th Chinese (pp. 6372–6376). IEEE

  13. Janssens O, Schulz R, Slavkovikj V, Stockman K, Loccufier M, Van de Walle R, Van Hoecke S (2015) Thermal image based fault diagnosis for rotating machinery. J Infrared Phys Technol 73:78–87

    Article  Google Scholar 

  14. Liu H, Xie T, Ran J, Gao S (2017) An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image. In Journal of Physics: Conference Series (Vol. 910, No. 1, p. 012031). IOP Publishing

  15. Lizák F, Kolcun M (2008) Improving reliability and decreasing losses of the electrical system with infrared thermography. Acta Electrotechnica et Informatica 8(1):60–63

  16. Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary dragonfly algorithm for feature selection. In New Trends in Computing Sciences (ICTCS), 2017 International Conference on (pp. 12–17). IEEE

  17. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. J Neural Comput Applic 27(4):1053–1073

    Article  Google Scholar 

  18. Mlakić D, Nikolovski S, Baus Z (2017) Detection of faults in electrical panels using deep learning method. In Smart Systems and Technologies (SST), 2017 International Conference on (pp. 55–61). IEEE

  19. Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. International journal of scientific and research publications 3(5):1

  20. Pareek S, Sharma R, Maheshwari R (2017) Application of artificial neural networks to monitor the thermal condition of electrical equipment. In Condition Assessment Techniques in Electrical Systems (CATCON), 2017 3rd International Conference on pp. 183–187

  21. Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. International journal of scientific and research publications 3(5):1

  22. Sales RBC, Pereira RR, Aguilar MTP, Cardoso AV (2017) Thermal comfort of seats as visualized by infrared thermography. Appl Ergon 62:142–149

    Article  Google Scholar 

  23. Szafron C (2008) Application of thermal imaging in electrical equipment examination. Available: zet10. ipee. pwr. wroc. pl/record/181/files/39. pdf, pp.1–3

  24. Ullah I, Yang F, Khan R, Liu L, Yang H, Gao B, Sun K (2017) Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach Energies, 10(12), 1–13

  25. Vantuch T, Fulneček J, Holuša M, Mišák S, Vaculík J (2018) An Examination of Thermal Features’ Relevance in the Task of Battery-Fault Detection. Appl Sci 8(2):182

    Article  Google Scholar 

  26. Zhang L, Mistry K, Lim CP, Neoh SC (2017) Feature selection using firefly optimization for classification and regression models. Decision Support Systems

  27. Zheng Z, Li Z, Nagar A (2015) Compact Deep Neural Networks for Device-Based Image Classification. In Mobile Cloud Visual Media Computing (pp. 201–217). Springer, Cham

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jansons Institute of Technology, Coimbatore, Tamil Nadu, India

    Shanmugam Chellamuthu

  2. Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India

    E. Chandira Sekaran

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  1. Shanmugam Chellamuthu
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  2. E. Chandira Sekaran
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Correspondence to Shanmugam Chellamuthu.

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Chellamuthu, S., Sekaran, E.C. Fault detection in electrical equipment’s images by using optimal features with deep learning classifier. Multimed Tools Appl 78, 27333–27350 (2019). https://doi.org/10.1007/s11042-019-07847-z

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  • DOI: https://doi.org/10.1007/s11042-019-07847-z

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