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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.

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. [ABSTRACT FROM AUTHOR]