AMERICAN ACADEMIC PUBLISHERS
http://www.academicpublishers.org
5
INTERNATIONAL JOURNAL OF SIGNAL PROCESSING,
EMBEDDED SYSTEMS AND VLSI DESIGN (ISSN: 2693-3861)
Volume 05, Issue 01, 2025, pages 05-09
Published Date: - 01-05-2025
A Deep Learning Approach to Electromagnetic Compatibility Test
Signal Prediction Using LSTM Networks
Ravi Patel
Department of Electronics, National Institute of Technology, Trichy, India
Abstract
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.
INTRODUCTION
Electromagnetic Compatibility (EMC) is a fundamental aspect of electronic system design, ensuring that different devices and
systems can coexist in the same electromagnetic environment without causing harmful interference to each other [1]. Achieving
EMC involves managing both electromagnetic emissions (unintentional generation of electromagnetic energy) and
electromagnetic immunity (the ability to withstand external electromagnetic disturbances) [1]. Regulatory bodies worldwide
mandate EMC testing for a vast range of electronic products before they can be placed on the market.
EMC testing encompasses various procedures, including conducted emissions and immunity tests, and radiated emissions and
immunity tests. Many of these tests involve the generation and application of specific electromagnetic signals. For example,
immunity tests might subject a device to transient pulses (e.g., electrostatic discharge, electrical fast transients, surges), voltage
dips and interruptions, or radio-frequency electromagnetic fields that are often amplitude or frequency modulated [1]. The
characteristics of these test signals, such as their amplitude, rise time, duration, frequency, and modulation parameters, are
precisely defined by international standards. However, ensuring the accuracy and consistency of these signals during testing,
especially when considering the interaction with the test setup and the device under test, can be complex. Furthermore, predicting
how these signals might behave under slightly different test conditions or extrapolating limited measurement data could provide
valuable insights for test planning and troubleshooting.
Traditional signal processing techniques, including methods for band-limited signal extrapolation [7, 11, 12, 13, 14], time-
frequency analysis, and statistical modeling [6], have been applied to analyze various types of signals. However, EMC test
AMERICAN ACADEMIC PUBLISHERS
http://www.academicpublishers.org
6
signals, particularly those encountered in real-world test environments, can exhibit non-linear behaviors or be influenced by
complex interactions that are difficult to capture with linear or simple statistical models [5, 6]. The ability to accurately predict
or model the non-linear dynamics of such signals is crucial for advanced EMC analysis and testing [2].
In recent years, deep learning has revolutionized many fields, including time-series analysis and prediction [2, 4, 5, 6, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19]. Recurrent Neural Networks (RNNs), and specifically Long Short-Term Memory (LSTM)
networks, are particularly well-suited for processing sequential data because of their ability to learn and remember dependencies
over long sequences [15]. LSTMs have shown success in diverse time-series prediction tasks, from natural language processing
[15] and speech processing [15, 25] to fault diagnosis [16] and radar signal analysis [18, 19]. Their capacity to model complex,
non-linear temporal patterns makes them a promising tool for analyzing and predicting EMC test signals.
This article proposes a conceptual framework for developing an EMC test signal prediction method based on LSTM networks.
We outline how LSTM can be applied to model the temporal characteristics of EMC test signals, discuss the necessary steps for
data preparation and model training, and explore the potential benefits and challenges of this approach for enhancing EMC testing
efficiency and understanding.
METHODOLOGY
The proposed methodology for predicting EMC test signals using LSTM networks involves several stages, from data acquisition
and preprocessing to model design, training, and evaluation. The core idea is to train an LSTM model to learn the underlying
temporal patterns of EMC test signals based on historical data, enabling it to predict future signal values or characteristics.
1.
Data Acquisition: The first step requires obtaining a dataset of EMC test signals. This data could be acquired through:
o
Experimental Measurements: Capturing the actual voltage or current waveforms of EMC test signals generated by test
equipment (e.g., surge generators, ESD simulators, RF signal generators) under controlled conditions [1]. Measurements should
be taken with appropriate high-bandwidth oscilloscopes or digitizers. Varying test parameters (e.g., voltage levels, pulse
repetition rates, modulation frequencies) would generate a diverse dataset.
o
Simulated Data: Generating synthetic EMC test signal waveforms using established mathematical models or simulation
tools [1]. While simulations provide clean data, they may not fully capture real-world imperfections or interactions with the test
setup.
o
Hybrid Approach: Combining experimental measurements and simulated data to leverage the realism of the former and
the control/scalability of the latter.
The data should be collected as time series, representing the signal amplitude (voltage or current) over time.
2.
Data Preprocessing: Raw time-series data needs to be preprocessed to be suitable for LSTM training:
o
Normalization/Scaling: Scaling the signal amplitudes to a consistent range (e.g., [0, 1] or [-1, 1]) is crucial for optimal
neural network performance [20, 21].
o
Windowing: Time-series data is typically structured into input-output pairs for supervised learning. This involves
creating "windows" of past signal data as input features to predict the signal value(s) in a future window. The size of the input
and output windows are hyperparameters that need to be tuned.
o
Splitting: The dataset is split into training, validation, and testing sets to train the model, tune hyperparameters, and
evaluate performance on unseen data.
3.
LSTM Model Architecture: A standard LSTM network architecture is proposed for this task [15].
o
Input Layer: Receives the preprocessed time-series input window. The input shape will be (batch size, input window
size, number of features), where the number of features is typically 1 for a single-channel signal.
o
LSTM Layers: One or more layers of LSTM units [15]. LSTM units contain internal gates (input, forget, output) and a
cell state that allow them to selectively remember or forget information over time, making them effective at capturing long-term
dependencies in sequences [15]. The number of LSTM layers and the number of units in each layer are hyperparameters.
Bidirectional LSTMs (BiLSTMs) [18, 19], which process the sequence in both forward and backward directions, could also be
explored to capture context from both past and future points in the input window.
o
Dense (Output) Layer: A fully connected layer that maps the output of the LSTM layers to the desired prediction output.
For predicting the next signal value, this would be a single unit. For predicting a future sequence, it would have a number o f
units equal to the output window size.
o
Activation Functions: Standard activation functions like ReLU or tanh can be used within the network.
4.
Model Training:
o
Loss Function: A suitable loss function is chosen to quantify the difference between the model's predictions and the
actual future signal values. Common choices for regression tasks include Mean Squared Error (MSE) or Mean Absolute Error
(MAE) [25].
o
Optimizer: An optimization algorithm (e.g., Adam, RMSprop) is used to update the model's weights during training to
minimize the loss function.
o
Hyperparameter Tuning: Key hyperparameters, such as the number of LSTM layers, units per layer, learning rate, batch
size, input/output window sizes, and the number of training epochs, need to be optimized [20, 21]. Techniques like Bayesian
optimization [20, 21] or grid search can be employed.
AMERICAN ACADEMIC PUBLISHERS
http://www.academicpublishers.org
7
o
Training Process: The model is trained iteratively on the training data, with performance monitored on the validation
set to prevent overfitting.
5.
Model Evaluation:
The trained model's performance is evaluated on the unseen test set using appropriate metrics:
o
Prediction Accuracy: Metrics like MSE, MAE, Root Mean Squared Error (RMSE), or R-squared can quantify how
closely the predicted signal matches the actual signal [25].
o
Signal Fidelity: Visual comparison of predicted and actual signal waveforms is crucial to assess whether the model
captures the shape, peaks, and transient features accurately.
o
Statistical Power: While Cohen's work [28] is in behavioral sciences, the concept of statistical power (the probability of
correctly rejecting a false null hypothesis) is relevant in evaluating if observed differences in prediction accuracy between models
are statistically significant.
The proposed methodology leverages the strengths of LSTM networks in handling sequential data to learn the intricate temporal
dynamics of EMC test signals. This approach is distinct from applying ML for condition monitoring [3, 4] or general time-series
analysis [5, 6] by specifically targeting the prediction of standardized or measured EMC waveforms.
Expected Outcomes and Advantages
Applying the outlined LSTM-based methodology to EMC test signal prediction is expected to yield several beneficial outcomes
and offer distinct advantages for EMC testing and analysis:
1.
Accurate Signal Prediction: The primary expected outcome is the ability of the trained LSTM model to accurately
predict future values of EMC test signals based on a sequence of past observations. Given LSTMs' proven capability in capturing
complex temporal patterns in various time series [15, 16, 17, 18, 19], it is anticipated that they can effectively model the
characteristics of standard EMC waveforms, including transient pulses with sharp rise times and oscillatory components, as well
as modulated signals.
2.
Improved Understanding of Signal Behavior: By training the model on data from EMC test equipment, the LSTM
network implicitly learns the underlying signal generation process and its dynamics. Analysis of the model's performance under
different conditions could potentially provide new insights into how test parameters influence the generated waveform,
complementing traditional signal analysis methods [7, 11, 12, 13, 14].
3.
Enhanced Test Efficiency: Accurate prediction capabilities could lead to more efficient EMC testing. For instance, if a
partial measurement of a long-duration test signal is obtained, the LSTM could potentially extrapolate the remainder of the
waveform [7, 11, 12, 13, 14], reducing the required measurement time. This could be particularly valuable for tests involving
long-duration modulated signals or complex sequences.
4.
Virtual Test Scenarios and Simulation: A well-trained model could potentially be used to generate synthetic EMC test
signal data [24] for simulating test scenarios or evaluating device behavior in a virtual environment. This could reduce the need
for physical test equipment in early design phases.
5.
Optimization of Test Parameters: By predicting signal characteristics across a range of input parameters, the model
could help identify optimal test settings or predict the outcome of tests before they are physically performed, leading to more
streamlined test plans.
6.
Adaptation to Non-Standard Conditions: While EMC tests follow standards [1], real-world environments or specific
test setups can introduce variations. An LSTM trained on measured signals could potentially predict the actual signal delivered
to the device under test, accounting for setup-specific effects.
The use of LSTM specifically addresses the sequential and potentially non-linear nature of EMC test signals, offering an
advantage over methods less suited to such data [2, 5, 6]. While other neural networks like CNNs [10] or hybrid models [17]
exist for time-series analysis, LSTMs are a strong baseline for sequences [15]. The interpretability of deep learning models is an
ongoing research area [24], but the focus here is on the predictive power for the signal itself.
DISCUSSION
The conceptual framework for using LSTM networks to predict EMC test signals presents a promising avenue for enhancing the
efficiency and effectiveness of EMC testing. The inherent capability of LSTMs to learn from and predict complex time-series
data [15, 16, 17, 18, 19] aligns well with the temporal nature of EMC test waveforms. By moving towards predictive capabilities,
the EMC testing process can potentially become more proactive, allowing for better resource allocation and faster identification
of potential compliance issues.
The ability to accurately predict signal characteristics could reduce the reliance on extensive physical measurements, particularly
for repetitive tests or when extrapolating from limited data points [7, 11, 12, 13, 14]. This is especially relevant in today's fast-
paced product development cycles where reducing time-to-market is critical. Furthermore, generating realistic synthetic test
signals based on a predictive model could provide valuable data for simulation and virtual validation, complementing physical
testing [24].
However, several challenges must be addressed to successfully implement this methodology:
1.
Data Quality and Quantity: Training deep learning models like LSTMs requires large amounts of high-quality data [4,
15]. Acquiring sufficiently diverse and accurate time-series data for various EMC test signals and test conditions can be resource-
AMERICAN ACADEMIC PUBLISHERS
http://www.academicpublishers.org
8
intensive. The data must capture the full range of variability and potential non-linearities present in real-world test signals.
2.
Model Generalization: Ensuring that the trained model generalizes well to unseen test conditions or even slightly
different test equipment is crucial. Overfitting to the training data is a risk that needs to be mitigated through proper validation
and regularization techniques [20, 21].
3.
Computational Resources: Training LSTM networks, especially deep architectures, can require significant
computational resources. While inference (making predictions) might be less demanding, deploying such models for real-time
prediction in a test environment requires careful consideration of hardware capabilities.
4.
Defining Prediction Targets: The specific prediction task needs to be clearly defined. Is the goal to predict the entire
future waveform, specific signal parameters (e.g., peak amplitude, duration), or the signal's behavior under a change in test setup?
The complexity of the model and the required data will vary depending on the target.
5.
Uncertainty Quantification: Providing a measure of uncertainty alongside the prediction is important for practical
applications [23]. Understanding the confidence level of a prediction is crucial for relying on it in a compliance testing context.
6.
Integration with Test Systems: Seamless integration of the prediction model with existing EMC test equipment and
software is necessary for practical deployment.
Future research should focus on implementing the proposed framework and validating its performance with extensive
experimental data from various EMC test types and equipment. Comparative studies evaluating the LSTM approach against
other time-series prediction methods, including traditional techniques [6, 7, 11, 12, 13, 14] and other machine learning models
like CNNs [10] or Transformers, would provide valuable benchmarks. Exploring techniques for online learning or transfer
learning could help the model adapt to new test environments or equipment without requiring complete retraining. Investigating
the potential for using these predictive models to inform adaptive testing strategies, where the test sequence or parameters are
adjusted based on real-time predictions, is another exciting avenue. Finally, considering the security implications of using
predictive models, such as potential model inversion attacks [24], is important for ensuring the integrity of the testing process.
The legal and safety aspects of AI-based systems [26] are also relevant if such models were to influence certification processes.
CONCLUSION
Electromagnetic Compatibility testing is vital for electronic product development, and the ability to accurately predict the
characteristics of EMC test signals holds significant potential for improving test efficiency and understanding. This article has
outlined a conceptual methodology for leveraging Long Short-Term Memory (LSTM) networks [15, 16, 17, 18, 19] to predict
these complex time-series signals. By learning the temporal dynamics of EMC waveforms from historical data, an LSTM model
can potentially provide accurate predictions, enabling faster testing, better test planning, and the possibility of generating
synthetic data for simulation [24]. While challenges related to data acquisition, model generalization, and computational
resources exist, the strengths of LSTMs in handling sequential and non-linear data make them a promising tool for this
application. Further research and implementation are needed to validate this approach and realize its full potential in the field of
electromagnetic compatibility.
REFERENCES
1.
Paul, C. R., Scully, R. C., Steffka, M. A. (2022). Introduction to electromagnetic compatibility[M]. John Wiley & Sons.
2.
Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modelling
(No. LA-UR-87-2662; CONF-8706130-4).
https://www.osti.gov/biblio/5470451
3.
Yang, J., Zhang, Y. (2005). Application research of support vector machines in condition trend prediction of mechanical
equipment. In Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in
Computer Science, vol. 3498. Springer.
https://doi.org/10.1007/11427469_136
4.
Yan, Z., Zhang, H., Sun, J., Yang, Y., & Xia, G. (2020). Development and application of aero-engine experimental data
mining algorithm library. In International Conference on Sensing, Measurement & Data Analytics in the era of Artificial
Intelligence (ICSMD) (pp. 264–269).
https://doi.org/10.1109/ICSMD50554.2020.9261719
5.
Wang, Y., Zhang, H., Shi, Z., et al. (2023). Nonlinear time series analysis and prediction of general aviation accidents
based on multi-timescales[J]. Aerospace, 10(8), 714.
6.
Spiridonakos, M. D., & Fassois, S. D. (2014). Non-stationary random vibration modelling via FS-TARMA models[J].
Mechanical Systems and Signal Processing, 47(1–2), 175–224.
7.
Brodzik, A. K., & Tolimieri, R. (2000). Extrapolation of band-limited signals and the finite Zak transform[J]. Signal
Processing, 80(3), 413–423.
8.
Wei, F., Liang, S., Zou, L. Y., et al. (2023). Extrapolation method of precipitation nowcasting radar echo based on GCA-
ConvLSTM prediction network[J]. Torrential Rain and Disasters, 42(4), 427–436.
9.
Shi, E., Li, Q., Gu, D., et al. (2018). A method of weather radar echo extrapolation based on convolutional neural
networks[C]//MMM. Springer, 2018, 16–28.
AMERICAN ACADEMIC PUBLISHERS
http://www.academicpublishers.org
9
10.
Kiranyaz, S., Avci, O., Abdeljaber, O., et al. (2021). 1D convolutional neural networks and applications: A survey[J].
Mechanical Systems and Signal Processing, 151, 107398.
https://doi.org/10.1016/j.ymssp.2021.107398
11.
Zhang, F. Q., Li, J. R., Wang, G. F., et al. (2015). Micro signal spectrum estimation based on derivative extrapolation
algorithm study[J]. Journal of Coal Technology, 5, 83–85.
https://doi.org/10.13301/j.cnki.ct.2015.05.031
12.
Bhatti, O. W., Torun, H. M., & Swaminathan, M. (2022). HilbertNet: A probabilistic framework for frequency response
extrapolation[J].
IEEE
Transactions
on
Electromagnetic
Compatibility,
64(2),
405–417.
https://doi.org/10.1109/TEMC.2021.3119277
