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Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning algori...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460744/ https://www.ncbi.nlm.nih.gov/pubmed/36080840 http://dx.doi.org/10.3390/s22176383 |
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author | Cortes-Aguilar, Teth Azrael Cantoral-Ceballos, Jose Antonio Tovar-Arriaga, Adriana |
author_facet | Cortes-Aguilar, Teth Azrael Cantoral-Ceballos, Jose Antonio Tovar-Arriaga, Adriana |
author_sort | Cortes-Aguilar, Teth Azrael |
collection | PubMed |
description | Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning algorithms that predict link quality (LQE) to save time, hence reducing expenses by early failure detection and problem prevention. Indeed, alarm signals used in conjunction with LQE classification models represent a novel paradigm for ANDON towers, allowing low-cost remote sensing within industrial environments. In this research, we propose a deep learning model, suitable for implementation in small workshops with limited computational resources. As part of our work, we collected a novel dataset from a realistic experimental scenario with actual industrial machinery, similar to that commonly found in industrial applications. Then, we carried out extensive data analyses using a variety of machine learning models, each with a methodical search process to adjust hyper-parameters, achieving results from common features such as payload, distance, power, and bit error rate not previously reported in the state of the art. We achieved an accuracy of 99.3% on the test dataset with very little use of computational resources. |
format | Online Article Text |
id | pubmed-9460744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94607442022-09-10 Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models Cortes-Aguilar, Teth Azrael Cantoral-Ceballos, Jose Antonio Tovar-Arriaga, Adriana Sensors (Basel) Article Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning algorithms that predict link quality (LQE) to save time, hence reducing expenses by early failure detection and problem prevention. Indeed, alarm signals used in conjunction with LQE classification models represent a novel paradigm for ANDON towers, allowing low-cost remote sensing within industrial environments. In this research, we propose a deep learning model, suitable for implementation in small workshops with limited computational resources. As part of our work, we collected a novel dataset from a realistic experimental scenario with actual industrial machinery, similar to that commonly found in industrial applications. Then, we carried out extensive data analyses using a variety of machine learning models, each with a methodical search process to adjust hyper-parameters, achieving results from common features such as payload, distance, power, and bit error rate not previously reported in the state of the art. We achieved an accuracy of 99.3% on the test dataset with very little use of computational resources. MDPI 2022-08-24 /pmc/articles/PMC9460744/ /pubmed/36080840 http://dx.doi.org/10.3390/s22176383 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cortes-Aguilar, Teth Azrael Cantoral-Ceballos, Jose Antonio Tovar-Arriaga, Adriana Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title | Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title_full | Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title_fullStr | Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title_full_unstemmed | Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title_short | Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models |
title_sort | link quality estimation for wireless andon towers based on deep learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460744/ https://www.ncbi.nlm.nih.gov/pubmed/36080840 http://dx.doi.org/10.3390/s22176383 |
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