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This is a Experimental Study study.

September 28, 2025Open Access

Electromagnetic Compatibility Issues and Solutions Based on Deep Learning

WLWei LiuTiangong University JZJunhao ZengKangmei Pharmaceutical (China)

Key Points

Improved accuracy in electromagnetic compatibility issue identification increases engineering efficiency, enhancing product reliability.
Experimental results demonstrate significant advancements in anomaly detection and problem classification methodology.
The study employs a multifaceted approach using autoencoders and convolutional networks for effective EMC diagnostics.
Findings support the integration of deep learning techniques in electromagnetic compatibility engineering, providing robust decision-making tools.

Abstract

This study explores the application of deep learning techniques in diagnosing and addressing electromagnetic compatibility (EMC) issues. To tackle challenges in anomaly detection, problem classification and localization, and multidimensional data analysis within EMC testing and diagnosis, three deep learning-based approaches are proposed: an anomaly detection model based on autoencoders, a multi-task convolutional neural network for problem classification and localization, and a multidimensional analysis system that combines LSTM, CNN, and GNN. Experimental results show that these methods significantly improve the accuracy of EMC issue identification and processing efficiency. This research provides a robust decision support tool for EMC engineering, with significant implications for enhancing the electromagnetic compatibility of electronic products.

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