Abstract
With the development and advancement of machine learning (ML), different aspects of our daily lives are now changed and revolutionized. Diverse ML-based smart and intelligent algorithms are deployed to detect faults in wires, especially in power cables. These approaches are beneficial for reliable, and better wire designs as some of them can estimate marks before they happen. Different sensing devices are made with the integration of intelligent ML-based methods to make the traditional fault detection methods very effective and productive. With the integration of machine learning and artificial intelligence-based architectures, fault detection is now very developed and efficient. Due to the deployment of these technologies in various sectors like power transmission networks, many people's lives can be saved. These intelligent procedures primarily work in real-time and can provide assistance and guidance within no time during any unwanted situation. Current research has considered that the Byol algorithm is used to detect wire damage for safety procedures. The experimental work was done, and the applications of the algorithm in the area of research show the effectiveness of the study. In this study, various parametric measures were used for the validation of the study.
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Guo, Z., Wang, C., Tian, Y. et al. Application of machine learning in wire damage detection for safety procedure. Soft Comput 26, 10623–10631 (2022). https://doi.org/10.1007/s00500-022-06747-z
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DOI: https://doi.org/10.1007/s00500-022-06747-z