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

Articolo
Data di Pubblicazione:
2024
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.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
CNN; current reconstruction; deep learning; magnetic field measurements; spectrogram
Elenco autori:
Lalla, A.; Albini, A.; Di Barba, P.; Mognaschi, M. E.
Autori di Ateneo:
DI BARBA PAOLO
LALLA ABDERRAOUF
MOGNASCHI MARIA EVELINA
Link alla scheda completa:
https://iris.unipv.it/handle/11571/1501106
Pubblicato in:
SENSORS
Journal
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