Detection of Eyebolt Faults Using a Random Forest Ensemble Model Based on Multiple High-Frequency Electromagnetic Parameters

Silva Filho, H. V. H.; Santos, R.G. M. dos; Barbosa, Douglas C. P.; Melo, M. T. de; Novo, Lauro R. G. S. Lourenço

doi:10.1590/2179-10742023v22i3271067

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications

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Article • J. Microw. Optoelectron. Electromagn. Appl. 22 (3) • Sept 2023 • https://doi.org/10.1590/2179-10742023v22i3271067 copy

Detection of Eyebolt Faults Using a Random Forest Ensemble Model Based on Multiple High-Frequency Electromagnetic Parameters

Authorship SCIMAGO INSTITUTIONS RANKINGS

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.

Index Terms
Anchoring eye bolts; fault detection; random forest algorithm and transmission lines.

Ref	Structure	Objective	Signal	Parameter	ML Algorithm
[⁴[4] D. C. P. Barbosa; L. H. A. de Medeiros; M. T. de Melo; L. R. G. S. Lourenço Novo; M. S. Coutinho; M. M. Alves, H. B. D. T. Lott Neto, P. H. R. P. Gama, ; R. G. M. dos Santos and V. L. Tarragô, “Machine Learning Approach to Detect Faults on the Anchor Rods of Power Transmission Lines”, IEEE Antennas and Wireless Propagation Letters, Vol. 18, pp. 2335-2339, 2019. https://doi.org/10.1109/LAWP.2019.2932052 https://doi.org/10.1109/LAWP.2019.293205... ]	Anchor rod	Fault Detection	Microwave	\|S11\|	Logistic Regression K-NNs Random Forest SVM ANN
[⁵[5] D. C. P. Barbosa; L. H. A. de Medeiros; M. T. de Melo; L. R. G. S. Lourenço Novo; M. S. Coutinho; M. M. Alves; R. G. M. dos Santos; V. L. Tarragô H. B. D. T. Lott Neto and P. H. R. P. Gama, “An electromagnetic multi-parameter strategy to detect faults in anchor rods using neural networks”. 2019 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC), p.3, 2019.]	Anchor rod	Fault Detection	Microwave	S11 VSWR Input Impedance	ANN
[⁸[8] Nicholas Durham , Junhui Zhao , Gregory Bridges and Douglas Thomso, “Acoustic guided wave detection of grounding rod corrosion: equivalent circuit model and implementation”. Smart Materials and Structures, Vol. 29, p. 55040, 2020. https://doi.org/10.1088/1361-665X/ab72e6. https://doi.org/10.1088/1361-665X/ab72e6... ]	Ground rod	Corrosion detection	Longitudinal acoustic guided waves	Pulse-echo	None
[⁹[9] N. Hirai, Y. Ohki, “Highly sensitive detection of distorted points in a cable by frequency domain reflectometry”, Proceedings of 2014 International Symposium on Electrical Insulating Materials, pp. 144-147, 2014. https://doi.org/10.1109/ISEIM.2014.6870741. https://doi.org/10.1109/ISEIM.2014.68707... ]	Coaxial cable	Fault location	Microwave	Impedance in the frequency domain	None
[¹⁰[10] Q. Shi, O. Kanoun, “Wire fault diagnosis in the frequency domain by impedancespectroscopy”, IEEE Transactions on Instrumentation and Measurement, Vol 64, pp. 2178-2187, 2015.https://doi.org/2179-2187.10.1109/TIM.2014.2386918. https://doi.org/2179-2187.10.1109/TIM.20... ]	Coaxial cable	Fault location	Microwave	Impedance Spectroscopy	None
[¹¹[11] C. Furs, P. Smith, and M. Diamond, “Feasibility of Reflectometry for Nondestructive Evaluation of Prestressed Concrete Anchors”, IEEE Sensors Journal, Vol. 9, pp. 1322 - 1329, 2009. https://doi.org/10.1109/JSEN.2009.2019309. https://doi.org/10.1109/JSEN.2009.201930... ]	Steel cable	Fault location	Microwave	Γ (Reflection coefficient)	None
[¹²[12] Ruslee Sutthaweekul and Gui Y. Tian, Steel Corrosion Stages Characterization Using Open Ended Rectangular Waveguide Probe. IEEE Sensors Journal, Vol. 18, pp. 1054 - 1062, 2018. https://doi.org/10.1109/JSEN.2017.2775521. https://doi.org/10.1109/JSEN.2017.277552... ]	Steel in Concrete	Characterization of steel corrosion stages	Microwave	\|S11\|	None
[¹³[13] K. Bouzaffour, B. Lescop, P. Talbot, F. Gallée, and S. Rioual, “Development of an Embedded UHF-RFID Corrosion Sensor for Monitoring Corrosion of Steel in Concrete”, IEEE Sensors Journal, Vol. 21, pp. 12306-12312, 2021. https://doi.org/10.1109/JSEN.2021.3064970. https://doi.org/10.1109/JSEN.2021.306497... ]	Steel in concrete	Corrosion detection	UHF	\|S11\|	None
[¹⁵[15] L. Novo, M. De Melo, M. De Oliveira, J. Bezerra, L. De Medeiros, R. Aquino, “Design connector for measurements in high frequency on anchor rods”, International Journal of Applied Electromagnetics and Mechanic, Vol. 45, pp. 457 - 464, 2014. https://doi.org/10.3233/JAE-141864. https://doi.org/10.3233/JAE-141864... ]	Anchor rod	Fault Detection	Microwave	\|S11\|	None
[¹⁶[16] M. S. Coutinho, L. R. G. S. Lourenço Novo, M. T. de Melo, L. H. A.de Medeiros, D. C. P. Barbosa, M. M. Alves, V. L. Tarragô, R. G. M. dos Santos, H. B. T. D. Lott Neto and P. H. R. P. Gama, “Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers”. Electric Power Systems Research, Vol. 194, p. 107106, 2021. https://doi.org/10.1016/j.epsr.2021.107106.347 . https://doi.org/10.1016/j.epsr.2021.1071... ]	Anchor rod	Fault Detection	Microwave	\|S11\|	Logistic Regression
[¹⁷[17] H. Zheng, S. Zhang, and X. Sun, “Classification recognition of anchor rod based on PSO-SVM”, 29th Chinese Control and Decision Conference (CCDC), pp. 2207-2212, 2017. https://doi.org/10.1109/CCDC.2017.7978881. https://doi.org/10.1109/CCDC.2017.797888... ]	Anchor rod	Fault Detection	Vibration	Signal characteristics (mean value, peak value, peak-to-peak value, standard deviation, kurtosis, root mean square value, etc.)	PSO-SVM
[¹⁹[19] H V H Silva Filho, Marcelo S Coutinho, M T de Melo, E M F de Oliveira, Vinícius L Tarragô, Lauro R G S Lourenco Novo, L M da Silva and Fabio N Fraga, “Design of a connector for fault detection in eye bolts used as insulator anchorages”, Engineering Research Express”, Vol. 2, p. 45021, 2020. https://doi.org/10.1088/2631-8695/abc9d0. https://doi.org/10.1088/2631-8695/abc9d0... ]	Eye bolt	Fault Detection	Microwave	\|S11\|	ANN
[²⁰[20] H. V. H. Silva Filho; D. C. P. Barbosa; M. S. Coutinho; M. T. de Melo, R. G. M. dos Santos, I. Llamas-Garro, “Reliable Structural Failure Detection in Eye Bolts using Reflectometry Signals”, 50 th European Microwave Conference (EuMC), pp. 1107-1110, 2021. https://doi.org/10-11.23919/EuMC48046.2021.9338147. https://doi.org/10-11.23919/EuMC48046.20... ]	Eye bolt	Fault Detection	Microwave	\|S11\|	None
Proposed System	Eye bolt	Fault Detection	Microwave	S11 VSWR Input Impedance	Random Forest

Parameter	Dimensions (mm)
Length (LE)	5, 25, 50, 75, and 100
Depth (DE)	0.1 to 8.0 with step 0.1
Depth (DE)	9, 10, 11, 12, and 13
Distance (DI)	75, 100, 125, 150, 175, 200 and 225

Bolt	Length (LE) (mm)	Depth (DE) (mm)	Distance (DI) (mm)
B0	Faultless sample
B1	50	8	70
B2	25	8	110
B3	50	2	110
B4	50	8	110
B5	100	8	110
B6	50	8	250

Accuracy	Precision (Prec)	Recall (Rec)	F1-Score
$\frac{t_{p} + t_{n}}{t_{p} + t_{n} + f_{p} + f_{n}}$	$\frac{t_{p}}{t_{p} + f_{p}}$	$\frac{t_{p}}{t_{p} + f_{n}}$	$2 ∙ \frac{P r e c ∙ R e c}{P r e c + R e c}$

EM Parameter	Mag.{S11}	Phase {S11}	Im{Z_in}	Re{Z_in}	VSWR
Pearson Correlation	0.948	0.997	0.992	0.904	0.909

Parameters	Accuracy (%)	F1-Score (%)	Rec (%)	Prec (%)
\|S11\|	96.76	98.13	95.08	99.94
Phase {S11}	96.61	97.88	96.53	99.82
VSWR	96.31	98.24	95.97	99.88
Re{Z_in}	96.41	97.39	96.08	99.88
Im {Z_in}	95.12	97.23	94.13	99.82
Ensemble	97.41	98.36	97.31	99.94

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