Evidence map›Paper›PMID 32823870›Full record

ArticleSensors (Basel, Switzerland)2020

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production.

Ana Lavalle, Miguel A Teruel, Alejandro Maté, Juan Trujillo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ana LavalleLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, Spain.ORCID 0000-0002-8399-4666
Miguel A TeruelLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, Spain.ORCID 0000-0003-0102-6918
Alejandro MatéLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, Spain.ORCID 0000-0001-7770-3693
Juan TrujilloLucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, Spain.ORCID 0000-0003-0139-6724

Funding

Spanish Center for Industrial Technological Development INNO-20171060Spanish Ministry of Science, Innovation, and Universities RTI2018-094283-B-C32University of Alicante I-PI 03-18
6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceBig Data analyticsdata visualizationgas turbinesInternet of Thingssustainable production

Identifiers

PMID32823870
PMCPMC7472268

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.