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- Title
Detection of Eyebolt Faults Using a Random Forest Ensemble Model Based on Multiple High-Frequency Electromagnetic Parameters.
- Authors
Silva Filho, H. V. H.; dos Santos, R. G. M.; Barbosa, Douglas C. P.; de Melo, M. T.; Lourenço Novo, Lauro R. G. S.
- Abstract
This paper presents an eyebolt structural fault detection system, based on the analysis of multiple electromagnetic parameters through a random forest classifier trained by both measurements and high-fidelity simulated signals. The proposed methodology is completely noninvasive and does not require the disassembly of the electrical infrastructure, allowing the live-line working. The obtained results show that the proposed multi-parameter strategy achieves high accuracy and increases the system's capability of detecting faults, improving the efficiency of the operator's preventive maintenance routines and, consequently, increasing the reliability of the power supply and energy distribution systems.
- Subjects
RANDOM forest algorithms; FAULT diagnosis; POWER resources; ELECTRIC fault location
- Publication
Journal of Microwaves, Optoelectronics & Electromagnetic Applications, 2023, Vol 22, Issue 3, p379
- ISSN
2179-1074
- Publication type
Article
- DOI
10.1590/2179-10742023v22i3271067