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Sensors as Drivers of Industry 4.0

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3221

Special Issue Editor


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Guest Editor
Design, Manufacturing and Engineering Management Department, The University of Strathclyde, Glasgow G1 1XJ, UK
Interests: Industry 4.0; IoT; AI/CI; big data analysis; cloud manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 rests on modern enabling technologies such as the Internet of Things (IoT), Additive Manufacturing (AM), Virtual and Augmented and Mixed Reality (AR/AR/MR), Cloud technologies, Artificial Intelligence (AI) and Machine Learning (ML).

All these technologies process data and a lot of that data stems from sensors, which are the drivers of Industry 4.0.

This Special Issue welcomes concise academic technical articles that explore all aspects of the data pipeline, from sensors to AI, the Cloud, and the end user. Theoretical as well as articles exploring practical applications are welcome.

Brief and well-focused literature review articles that contribute to the knowledge surrounding this topic are also acceptable.

Prof. Dr. Jorn Mehnen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor data
  • smart sensors
  • Internet of Things
  • artificial intelligence
  • machine learning
  • cloud
  • edge AI
  • AR/VR/MR
  • enabling technologies
  • Industry 4.0

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Published Papers (1 paper)

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Review

25 pages, 855 KiB  
Review
Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis
by Edwin Benito Mitacc Meza, Dalton Garcia Borges de Souza, Alessandro Copetti, Ana Paula Barbosa Sobral, Guido Vaz Silva, Iara Tammela and Rodolfo Cardoso
Sensors 2024, 24(19), 6457; https://doi.org/10.3390/s24196457 - 6 Oct 2024
Viewed by 2802
Abstract
The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in [...] Read more.
The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G. Full article
(This article belongs to the Special Issue Sensors as Drivers of Industry 4.0)
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