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

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 spec...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-03, Vol.24 (6), p.1798
Main Authors: Lalla, Abderraouf, Albini, Andrea, Di Barba, Paolo, Mognaschi, Maria Evelina
Format: Article
Language:English
Subjects:
Adaptability
Artificial intelligence
Automation
CNN
Communication
current reconstruction
Deep learning
Efficiency
Energy consumption
Machine learning
magnetic field measurements
Magnetic fields
Measurement techniques
Neural networks
Sensors
spectrogram
Waterfalls
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24061798