Low-Cost Device for Collecting Data from Acceleration Sensors

This article presents the development of a device for collecting data from acceleration sensors. The developed device uses a 32-bit ESP32 microcontroller, which offers good computational capabilities and rich communication peripherals. The current work examines the structure of the developed system, as well as its operational algorithm. The text presents the main components of the device and the method used for data acquisition. Vibration data was collected using a digital accelerometer. The configuration and parameterization of the device were carried out via a JSON file, which controlled the number of measurements and the rate at which they were performed. The acquired data can be easily filtered and processed using mathematical software, allowing it to be presented in a format suitable for further use in machine learning algorithms and artificial neural networks. The developed solution represents a low-cost alternative to similar vibration data acquisition systems, enabling condition monitoring of various machine components and predictive maintenance at a low hardware cost.

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Publication Details

Published
2025-08-25
DOI
https://doi.org/10.3390/engproc2025104010
Primary Topic
Astronomical Observations and Instrumentation
Type
article
Field-Weighted Citation Impact
0.00

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article

Low-Cost Device for Collecting Data from Acceleration Sensors

Stefan Ivanov
Astronomical Observations and Instrumentation
article

Low-Cost Device for Collecting Data from Acceleration Sensors

Stefan Ivanov
article en

Abstract

This article presents the development of a device for collecting data from acceleration sensors. The developed device uses a 32-bit ESP32 microcontroller, which offers good computational capabilities and rich communication peripherals. The current work examines the structure of the developed system, as well as its operational algorithm. The text presents the main components of the device and the method used for data acquisition. Vibration data was collected using a digital accelerometer. The configuration and parameterization of the device were carried out via a JSON file, which controlled the number of measurements and the rate at which they were performed. The acquired data can be easily filtered and processed using mathematical software, allowing it to be presented in a format suitable for further use in machine learning algorithms and artificial neural networks. The developed solution represents a low-cost alternative to similar vibration data acquisition systems, enabling condition monitoring of various machine components and predictive maintenance at a low hardware cost.

Technical University of Gabrovo (BG)
European Regional Development Fund
Openalex Percentile: Top 76%
Astronomical Observations and Instrumentation
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