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10.3390/s24061798
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Article

Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach

by
Abderraouf Lalla
,
Andrea Albini
,
Paolo Di Barba
and
Maria Evelina Mognaschi
*
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(6), 1798; https://doi.org/10.3390/s24061798
Submission received: 31 January 2024 / Revised: 1 March 2024 / Accepted: 7 March 2024 / Published: 11 March 2024
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, we present a deep learning approach for identifying current intensity and frequency. The reconstruction is based on measurements of the magnetic field generated by the current flowing in a conductor. Magnetic field data are collected using a magnetic probe capable of generating a spectrogram, representing the spectrum of frequencies of the magnetic field over time. These spectrograms are saved as images characterized by color density proportional to the induction field value at a given frequency. The proposed deep learning approach utilizes a convolutional neural network (CNN) with the spectrogram image as input and the current or frequency value as output. One advantage of this approach is that current estimation is achieved contactless, using a simple magnetic field probe positioned close to the conductor.
Keywords: magnetic field measurements; current reconstruction; spectrogram; deep learning; CNN magnetic field measurements; current reconstruction; spectrogram; deep learning; CNN

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MDPI and ACS Style

Lalla, A.; Albini, A.; Di Barba, P.; Mognaschi, M.E. Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach. Sensors 2024, 24, 1798. https://doi.org/10.3390/s24061798

AMA Style

Lalla A, Albini A, Di Barba P, Mognaschi ME. Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach. Sensors. 2024; 24(6):1798. https://doi.org/10.3390/s24061798

Chicago/Turabian Style

Lalla, Abderraouf, Andrea Albini, Paolo Di Barba, and Maria Evelina Mognaschi. 2024. "Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach" Sensors 24, no. 6: 1798. https://doi.org/10.3390/s24061798

APA Style

Lalla, A., Albini, A., Di Barba, P., & Mognaschi, M. E. (2024). Spectrogram Inversion for Reconstruction of Electric Currents at Industrial Frequencies: A Deep Learning Approach. Sensors, 24(6), 1798. https://doi.org/10.3390/s24061798

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