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    <title>Journal of Embedded Systems</title>
    <link>http://www.sciepub.com/journal/JES</link>
    <description>Journal of Embedded Systems is a peer-reviewed, open access journal that provides rapid publication of articles in all areas of embedded systems. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of embedded systems.</description>
    <dc:publisher>Science and Education Publishing</dc:publisher>
		<dc:language>en</dc:language>
		<dc:rights>2013 Science and Education Publishing Co. Ltd All rights reserved.</dc:rights>
		<prism:publicationName>Journal of Embedded Systems</prism:publicationName>
		6
		1
		January 2023
		<prism:copyright>2013 Science and Education Publishing Co. Ltd All rights reserved.</prism:copyright>
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<title>
Development of a Low-Cost IOT Device Focused on Protection and Monitoring Compressors in Refrigeration and Air-Conditioning Applications
</title>
<link>http://pubs.sciepub.com/jes/6/1/1</link>
<description>
<![CDATA[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.]]>
</description>
<dc:creator>
Nelson  Sierra, Felipe  Gonzalez
</dc:creator>
<dc:date>2023-07-26</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2023-07-26</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>6</prism:volume>
<prism:startingPage>1</prism:startingPage>
<prism:endingPage>8</prism:endingPage>
<prism:doi>10.12691/jes-6-1-1</prism:doi>
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