RESEARCH PAPER
Electromagnetic Relay Contact Resistance Prediction Based on TimeGAN with CNN-LSTM-Attention
1
College of Automation Engineering, Jiangsu University of Science and Technology, China
2
School of Electronic and Electrical Engineering, University of Leeds, United Kingdom
These authors had equal contribution to this work
Submission date: 2025-02-13
Final revision date: 2025-04-08
Acceptance date: 2025-05-03
Online publication date: 2025-05-22
Publication date: 2025-05-22
Corresponding author
Zhaobin Wang
College of Automation Engineering, Jiangsu University of Science and Technology, 212100, Zhenjiang, China
College of Automation Engineering, Jiangsu University of Science and Technology, 212100, Zhenjiang, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):204610
HIGHLIGHTS
- Proposes TimeGAN-CNN-LSTM-Attention model for precise contact resistance prediction.
- TimeGAN-generated synthetic data validated via PCA/KDE to resolve degradation data.
- Combines adversarial data generation and deep learning in resource-limited systems.
- Achieves 30.22% R² boost , demonstrating superior prediction accuracy.
KEYWORDS
TOPICS
ABSTRACT
Electromagnetic relays are critical in aerospace and military systems, affecting the safety and stability of applications like aircraft control, satellite communication, and missile launchers. However, the scarcity of degradation data and complex variations in contact resistance pose challenges. Traditional methods often struggle with small samples. To address these issues, we propose a novel framework integrating TimeGAN with a CNN-LSTM-Attention model. TimeGAN generates synthetic degradation data that aligns with the statistical distribution of the original dataset, mitigating data scarcity. Data quality is evaluated using PCA and KDE. The CNN-LSTM model captures multi-scale temporal features, while the attention mechanism highlights critical features to improve contact resistance prediction accuracy. Experimental results show that the proposed framework outperforms traditional methods, demonstrating robust performance even without data augmentation. These findings offer a valuable foundation for health monitoring and fault prediction in high-reliability systems.
ACKNOWLEDGEMENTS
The authors are grateful to the anonymous reviewers, and the editor for their critical and constructive review of the manuscript. This study was co-supported by the National Natural Science Foundation of China (51507074), Postgraduate Research &Practice Innovation Program of Jiangsu Province (KYCX25_4368).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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