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E. Winandy, C. Saavedra, and J. Lebrun, “Experimental analysis and simplified modelling of a hermetic scroll refrigeration compressor,” Applied thermal engineering, vol. 22, no. 2, pp. 107-120, 2002.

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Article

Development of a Low-Cost IOT Device Focused on Protection and Monitoring Compressors in Refrigeration and Air-Conditioning Applications

1Department of Electrical and Electronics Engineering, National University of Colombia Bogota DC, Colombia

2Department of Mechanics and Mechatronics Engineering, National University of Colombia Bogota DC, Colombia


Journal of Embedded Systems. 2023, Vol. 6 No. 1, 1-8
DOI: 10.12691/jes-6-1-1
Copyright © 2023 Science and Education Publishing

Cite this paper:
Nelson Sierra, Felipe Gonzalez. Development of a Low-Cost IOT Device Focused on Protection and Monitoring Compressors in Refrigeration and Air-Conditioning Applications. Journal of Embedded Systems. 2023; 6(1):1-8. doi: 10.12691/jes-6-1-1.

Correspondence to: Nelson  Sierra, Department of Electrical and Electronics Engineering, National University of Colombia Bogota DC, Colombia. Email: nasierras@unal.edu.co

Abstract

This project addresses the problem of correctly diagnosing compressors for air conditioning and refrigeration systems. The aim is to use readily available instrumentation from the global market to create a low-cost, accessible, and easy-to-assemble diagnostic solution for all types of users. In this work, we propose the use of an Espressif ESP32 controller, compatible with Arduino language, connected to Microsoft Azure. Additionally, we introduce algorithms based on trends that provide both basic and advanced users with real-time knowledge of relevant variables and detected failures in the refrigeration process. This enables users to anticipate and prevent failures through an enhanced condition-based maintenance scheme. The proposed solution aims to reduce incorrect diagnoses, minimize system downtime, and enhance troubleshooting capabilities, spare parts control, and product preservation. Furthermore, to expand the reach of this solution, the device can be utilized as an input for system data to generate valuable information for a recurrent neural network. This network can diagnose not only compressors but also the entire system, considering two significant variables: coefficient of performance and mass flow.

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