Work

Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines

Year: 2024

Type: article

Abstract: The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes.

Source: Machines

DOI: 10.3390/machines12080551

Author: Alessandro Massaro

Institution: LUM Jean Monnet University

Language: English

Topic: Analog and Mixed-Signal Circuit Design

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728