Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production
Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/16/4556 |
| _version_ | 1797558214679592960 |
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| author | Ana Lavalle Miguel A. Teruel Alejandro Maté Juan Trujillo |
| author_facet | Ana Lavalle Miguel A. Teruel Alejandro Maté Juan Trujillo |
| author_sort | Ana Lavalle |
| collection | DOAJ |
| description | Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented. |
| first_indexed | 2024-03-10T17:27:11Z |
| format | Article |
| id | doaj.art-20b1f47f59a3436d97efccf709be52a8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:27:11Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20b1f47f59a3436d97efccf709be52a82023-11-20T10:07:54ZengMDPI AGSensors1424-82202020-08-012016455610.3390/s20164556Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity ProductionAna Lavalle0Miguel A. Teruel1Alejandro Maté2Juan Trujillo3Lucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, SpainLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, SpainLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, SpainLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, SpainImproving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.https://www.mdpi.com/1424-8220/20/16/4556Internet of Thingsdata visualizationBig Data analyticssustainable productiongas turbinesArtificial Intelligence |
| spellingShingle | Ana Lavalle Miguel A. Teruel Alejandro Maté Juan Trujillo Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production Sensors Internet of Things data visualization Big Data analytics sustainable production gas turbines Artificial Intelligence |
| title | Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production |
| title_full | Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production |
| title_fullStr | Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production |
| title_full_unstemmed | Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production |
| title_short | Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production |
| title_sort | fostering sustainability through visualization techniques for real time iot data a case study based on gas turbines for electricity production |
| topic | Internet of Things data visualization Big Data analytics sustainable production gas turbines Artificial Intelligence |
| url | https://www.mdpi.com/1424-8220/20/16/4556 |
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