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

## TL;DR

This paper introduces a deep learning method to estimate electric current and frequency from magnetic field measurements using spectrogram images.

## Contribution

The novel contribution is a contactless CNN-based approach for reconstructing electric currents using magnetic field spectrograms.

## Key findings

- A CNN model was developed to estimate current intensity and frequency from spectrogram images.
- The method uses a magnetic probe to collect data without physical contact to the conductor.
- The approach demonstrates potential for industrial applications requiring non-invasive current monitoring.

## 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.