A Deep Learning Approach to Electromagnetic Compatibility Test Signal Prediction Using LSTM Networks

Аннотация

Electromagnetic Compatibility (EMC) testing is a critical step in the development and certification of electronic devices to ensure they function correctly in their intended electromagnetic environment without causing or being susceptible to unacceptable electromagnetic interference [1]. EMC tests often involve applying specific electromagnetic test signals and monitoring the device's response or measuring its emissions. These test signals, particularly those used for immunity testing (e.g., transient pulses, modulated sine waves), are inherently time-series data with complex temporal characteristics. Accurately predicting the behavior or required parameters of these test signals under various conditions or extrapolating limited measurements could significantly optimize testing procedures, reduce test time, and improve the efficiency of EMC compliance efforts [27]. Traditional signal processing techniques [7, 11, 12, 13, 14] may struggle with the non-linear and potentially non-stationary nature of some EMC phenomena and test signals [5, 6]. Deep learning, specifically Long Short- Term Memory (LSTM) networks, has demonstrated exceptional capabilities in modeling and predicting complex sequential data [15, 16, 17, 18, 19]. This article proposes and outlines a methodology for predicting EMC test signal characteristics using LSTM networks. We discuss the conceptual framework for data acquisition, model architecture design, training, and evaluation, drawing upon principles from time-series analysis [5, 6], neural networks [2, 4, 10, 15, 16, 17, 18, 19], and signal processing [7, 11, 12, 13, 14]. The potential benefits include enhanced test efficiency, improved understanding of signal behavior, and the possibility of generating synthetic test data for simulation purposes [24]. Keywords Electromagnetic Compatibility, Test Signal Prediction, LSTM Network, Deep Learning, Signal Processing, Electromagnetic Interference, Neural Networks, Machine Learning, Predictive Modeling, Electrical Engineering.