Insight on the Application of Deep Learning-Based Thermal Image Processing Methods in Electrical System Anomaly Detection and their Comparative Analysis

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 4

Year of Publication : 2024

Authors : Pankaj Chaudhari, Sajid Patel, Mohammedirfan I. Siddiqui, Nupur Sinha

10.14445/23488379/IJEEE-V11I4P126

How to Cite?

Pankaj Chaudhari, Sajid Patel, Mohammedirfan I. Siddiqui, Nupur Sinha, "Insight on the Application of Deep Learning-Based Thermal Image Processing Methods in Electrical System Anomaly Detection and their Comparative Analysis," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 4, pp. 240-253, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I4P126

Abstract:

Ensuring the reliability and safety of electrical systems necessitates constant inspections. However, manual inspections pose risks, are time-consuming, and are impractical for real-time monitoring. This paper presents a novel, noninvasive, and efficient approach for automated electrical system anomaly detection using deep learning and thermal image processing. We have proposed a Convolutional Neural Network (CNN) based framework utilizing the well-established GoogLeNet and other deep learning-based architecture to classify thermal images of electrical systems as “normal” or “abnormal.” This framework achieves a high accuracy of 99% in anomaly detection, surpassing traditional methods and paving the way for real-time monitoring and early fault identification.

Keywords:

Anomaly detection, CNN, Deep Learning, Electrical system inspection, Predictive maintenance, Thermal imaging.

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