2D image-based electrical contact surface degradation and its health assessment

Guojin Liu; Yuze Yang; Lekang Wang; Xu Liu; Xujing Wang

doi:10.1515/teme-2025-0003

Article

2D image-based electrical contact surface degradation and its health assessment

Guojin Liu
Guojin Liu received her Ph.D. degree in Electrical Engineering from Hebei University of Technology, Tianjin, China, where she is currently a Professor. Her research focuses on the reliability assessment, fault diagnosis, and health management of electrical and intelligent electrical equipment, with an emphasis on advanced measurement and analysis techniques.
, Yuze Yang
Yuze Yang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on image-based methods for the health assessment of electrical contacts.
, Lekang Wang
Lekang Wang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. His research focuses on reliability modeling of electrical contacts.
, Xu Liu
Xu Liu is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on surface texture analysis of electrical contacts.
and Xujing Wang
Xujing Wang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on the application of statistical methods in contact health assessment.

Published/Copyright: May 7, 2025

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From the journal tm - Technisches Messen Volume 92 Issue 6

Abstract

Assessing the degradation of electrical contact surfaces using three-dimensional (3D) measurements is often costly and complex, limiting their use in real-time monitoring. To address this, a two-dimensional (2D) image-based approach is proposed as an efficient and practical alternative. The method uses 2D texture features, such as Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), to quantify surface degradation. A novel Image-Based Degradation Index (IDI) is introduced, and its relationship with degradation extent is modeled using a Random Forest (RF) algorithm optimized by Particle Swarm Optimization (PSO). Validation against 3D data confirms the accuracy of 2D predictions. The results show that 2D imaging provides a reliable and cost-effective solution for assessing contact health, with strong agreement between 2D predictions and 3D measurements.

Zusammenfassung

Die Bewertung der Verschlechterung elektrischer Kontaktflächen durch dreidimensionale (3D) Messungen ist oft kostspielig und komplex, was ihre Verwendung bei der Echtzeitüberwachung einschränkt. Daher wird ein zweidimensionaler (2D), bildbasierter Ansatz als effiziente und praktische Alternative vorgeschlagen. Die Methode verwendet 2D-Texturmerkmale wie die Gray-Level Co-occurrence Matrix (GLCM) und Local Binary Patterns (LBP), um die Oberflächenverschlechterung zu quantifizieren. Ein neuartiger bildbasierter Degradationsindex (IDI) wird eingeführt, und seine Beziehung zum Degradationsausmaß wird mit einem Random Forest (RF) Algorithmus modelliert, der durch Partikelschwarmoptimierung (PSO) optimiert wird. Die Validierung anhand von 3D-Daten bestätigt die Genauigkeit der 2D-Vorhersagen. Die Ergebnisse zeigen, dass die 2D-Bildgebung eine zuverlässige und kosteneffiziente Lösung für die Bewertung des Zustands von Kontaktflächen darstellt, wobei eine hohe Übereinstimmung zwischen 2D-Vorhersagen und 3D-Messungen besteht.

Keywords: electrical contact degradation; 2D image analysis; texture feature extraction; health assessment

Schlüsselwörter: Degradation elektrischer Kontakte; 2D-Bildanalyse; Extraktion von Texturmerkmalen; Zustandsbewertung

Corresponding author: Yuze Yang, Hebei University of Technology, Tianjin, China, E-mail: yangyzeee@163.com

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 52377142

About the authors

Guojin Liu

Guojin Liu received her Ph.D. degree in Electrical Engineering from Hebei University of Technology, Tianjin, China, where she is currently a Professor. Her research focuses on the reliability assessment, fault diagnosis, and health management of electrical and intelligent electrical equipment, with an emphasis on advanced measurement and analysis techniques.

Yuze Yang

Yuze Yang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on image-based methods for the health assessment of electrical contacts.

Lekang Wang

Lekang Wang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. His research focuses on reliability modeling of electrical contacts.

Xu Liu

Xu Liu is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on surface texture analysis of electrical contacts.

Xujing Wang

Xujing Wang is a Master’s student in Electrical Engineering at Hebei University of Technology, Tianjin, China. Her research focuses on the application of statistical methods in contact health assessment.

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: ChatGPT was used to improve the language quality of the manuscript.
Conflict of interest: The authors state no conflict of interest.
Research funding: This research was supported by the National Natural Science Foundation of China (Grant No. 52377142).
Data availability: The data generated or analyzed during this study are confidential and intended solely for internal use by the research team and collaborating institutions. They will not be shared publicly.

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Received: 2025-01-15

Accepted: 2025-04-16

Published Online: 2025-05-07

Published in Print: 2025-06-26

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Articles in the same Issue

https://doi.org/10.1515/teme-2025-0003

Keywords for this article

electrical contact degradation; 2D image analysis; texture feature extraction; health assessment