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Sensors Application in Smart Factories

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

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 17723

Special Issue Editor


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Guest Editor
Department of Electronics, Polytechnic School, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Madrid, Spain
Interests: smart sensors for smartgrids and microgrids; smart grid communications; control electronics; real time systems; solar and wind energy control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to identify and discuss technical challenges and recent results related to smart factories (Industry 4.0). To meet the requirements of smart factories, new sensors, processing algorithms and advanced communication networks are needed. Improvements are also required in the areas of data storage and management, efficient monitoring, effective and flexible manufacturing resource management, better system scalability, and reconfiguration.  Therefore, the topics considered for this Special Issue range from smart sensors and embedded systems that already exist in the lower layer to data processing and cloud techniques in the upper layer, in order to achieve a better understanding, adjustment and performance of all industrial processes

In defining the scope of the Special Issue, we attempt to capture the main directions within Industry 4.0. There are many topics related to this Special Issue, such as the use of advanced sensors, control and mechatronics in manufacturing processes, extensive data collection and storage, extensive data analysis, feedback on industrial processes, resource support, new security challenges, etc. In addition, it can involve the so-called cybernetic physical systems, the modeling of systems through intelligent techniques and the optimization of processes in real time, among others

The topics of interests for this Special Issue include, but are not limited to:

  • Advanced sensors and systems in smart factory
  • Wireless sensors
  • Industrial internet of things
  • Mitigation of communication issues in industrial control networks 
  • Security in industrial sensor networks
  • Industrial information integration in smart factory
  • Smart sensors and data processing for energy management
  • Cloud computing techniques
  • Technologies for manufacturing cyber-physical systems
  • Collaborative intelligent devices.
  • Information coordination and interaction
  • Machine learning and decision science models for data analysis
  • Tools and technologies for deploying and managing big data
  • Security and privacy protection
Prof. Dr. Fco Javier Rodríguez
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.

Published Papers (3 papers)

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18 pages, 9503 KiB  
Article
A Case about the Upgrade of Manufacturing Equipment for Insertion into an Industry 4.0 Environment
by Marcelo A. García-Garza, Horacio Ahuett-Garza, Maria G. Lopez, Pedro Orta-Castañón, Thomas R. Kurfess, Pedro D. Urbina Coronado, David Güemes-Castorena, Salvador G. Villa and Sergio Salinas
Sensors 2019, 19(15), 3304; https://doi.org/10.3390/s19153304 - 27 Jul 2019
Cited by 12 | Viewed by 4852
Abstract
Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines [...] Read more.
Industry 4.0 is a synonym for the confluence of technologies that allows the integration of information technology, data science, and automated equipment, to produce smart industrial systems. The process of inserting new technologies into current conventional environments involves a wide range of disciplines and approaches. This article presents the process that was followed to identify and upgrade one station in an industrial workshop to make it compatible with the more extensive system as it evolves into the Industry 4.0 environment. An information processing kit was developed to upgrade the equipment from an automated machine to an Industry 4.0 station. The kit includes a structure to support the sensor and the data processing unit; this unit consisted of a minicomputer that records the data, graded the performance of the components, and sent the data to the cloud for storage, reporting, and further analysis. The information processing kit allowed the monitoring of the inspection system and improved the quality and speed of the inspection process. Full article
(This article belongs to the Special Issue Sensors Application in Smart Factories)
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36 pages, 6164 KiB  
Article
Smart Sensors Applications for a New Paradigm of a Production Line
by Marina Indri, Luca Lachello, Ivan Lazzero, Fiorella Sibona and Stefano Trapani
Sensors 2019, 19(3), 650; https://doi.org/10.3390/s19030650 - 05 Feb 2019
Cited by 38 | Viewed by 8179
Abstract
Industrial plants are going to face a deep renewing process within the Industry 4.0 scenario. New paradigms of production lines are foreseen in the very near future, characterized by a strict collaboration between humans and robots and by a high degree of flexibility. [...] Read more.
Industrial plants are going to face a deep renewing process within the Industry 4.0 scenario. New paradigms of production lines are foreseen in the very near future, characterized by a strict collaboration between humans and robots and by a high degree of flexibility. Such envisaged improvements will require the smart use of proper sensors at very different levels. This paper investigates three different aspects of this industrial renewing process, based on three different ways of exploiting sensors, toward a new paradigm of a production line. The provided contributions, offering various types of innovation and integration, are relative to: (i) a virtual sensor approach for manual guidance, increasing the potentialities of a standard industrial manipulator, (ii) a smart manufacturing solution to assist the operator’s activity in manual assembly stations, through an original exploitation of multiple sensors, and (iii) the development of an advanced robotic architecture for a flexible production line, in which a team of autonomous mobile robots acts as a meta-sensor net supporting traditional automated guided vehicles. Accurate analyses of existing state-of-the-art solutions compared with the proposed ones are offered for the considered issues. Full article
(This article belongs to the Special Issue Sensors Application in Smart Factories)
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22 pages, 2514 KiB  
Article
Predicting Emotion and Engagement of Workers in Order Picking Based on Behavior and Pulse Waves Acquired by Wearable Devices
by Yusuke Kajiwara, Toshihiko Shimauchi and Haruhiko Kimura
Sensors 2019, 19(1), 165; https://doi.org/10.3390/s19010165 - 04 Jan 2019
Cited by 12 | Viewed by 3870
Abstract
Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high [...] Read more.
Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets. Full article
(This article belongs to the Special Issue Sensors Application in Smart Factories)
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