Deep learning method for predicting electromagnetic emission spectrum of aerospace equipment

• Published in: IET science, measurement & technology Vol. 18; no. 4; pp. 193 - 201
• Main Author: Zhang, Yuting
• Format: Journal Article
• Language: English
• Published: Wiley 01.06.2024
• Subjects: aerospace testing; Bayes rule; data analysis; data decomposition; deep learning; electromagnetic compatibility; long short‐term memory; spectrum prediction
• DOI: 10.1049/smt2.12178
• ISSN: 1751-8822

This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment. Aiming at the typical and unpredictable spike signals in the electromagnetic compatibility test spectrum, a data decomposition method is adopted to extract such signals on the basis of a certain threshold and reconstruct them into a data sequence. After data decomposition, two data sequences are formed, and a long short‐term memory network is used for training and predicting the electromagnetic emission spectrum. In the process of network training, Bayesian optimization is used to optimize the network hyperparameter.