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Virtual Sensors for Industry 4.0 Era
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Virtual Sensors for Industry 4.0 Era

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 12891

Special Issue Editors

Prof. Dr. Nicolás Montés

E-Mail Website
Guest Editor
Technical School of Design, Architecture and Engineering, CEU Cardenal Herrera University, 46115 Alfara del Patriarca, Valencia, Spain
Interests: Industry 4.0; virtual sensors; Industrial Big Data; Digital Twin
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Chinesta

E-Mail Website
Guest Editor
ESI Chair, Arts et Metiers Institute of Technology, CNRS, CNAM, PIMM, HESAM Université, F-75013 Paris, France
Interests: algorithms; industrial design; bioengineering; aerospace; engineering; aeronautical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to highlight the different possibilities offered by Industry 4.0, such as Digital Twin or industrial Big Data to define virtual sensors that allow parameters of industrial lines to be measured, either to be able to carry out predictive maintenance, the quality of parts, etc., which would otherwise be too expensive due to the need to install a large number of sensors, or simply because there are no sensors to measure such parameters.

Prof. Dr. Nicolás Montés
Prof. Dr. Francisco Chinesta
Guest Editors

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

  • virtual sensors
  • Industry 4.0
  • Industrial Big Data
  • Digital Twin
  • Hybrid Twin
  • predictive maintenance
  • quality inspection

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Published Papers (4 papers)

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Research

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17 pages, 1312 KiB  
Article
Virtual Sensor of Gravity Centres for Real-Time Condition Monitoring of an Industrial Stamping Press in the Automotive Industry
by Ivan Peinado-Asensi, Nicolás Montés and Eduardo García
Sensors 2023, 23(14), 6569; https://doi.org/10.3390/s23146569 - 21 Jul 2023
Cited by 4 | Viewed by 1466
Abstract
This article proposes the development of a novel tool that allows real-time monitoring of the balance of a press during the stamping process. This is performed by means of a virtual sensor that, by using the tonnage information in real time, allows us [...] Read more.
This article proposes the development of a novel tool that allows real-time monitoring of the balance of a press during the stamping process. This is performed by means of a virtual sensor that, by using the tonnage information in real time, allows us to calculate the gravity centre of a virtual load that moves the slide up and down. The present development follows the philosophy shown in our previous work for the development of industrialised predictive systems, that is, the use of the information available in the system to develop IIoT tools. This philosophy is defined as I3oT (industrializable industrial Internet of Things). The tonnage data are part of a set of new criteria, called Criterion-360, used to obtain this information. This criterion stores data from a sensor each time the encoder indicates that the position of the main axis has rotated by one degree. Since the main axis turns in a complete cycle of the press, this criterion allows us to obtain information on the phases of the process and easily shows where the measured data are in the cycle. The new system allows us to detect anomalies due to imbalance or discontinuity in the stamping process by using the DBSCAN algorithm, which allows us to avoid unexpected stops and serious breakdowns. Tests were conducted to verify that our system actually detects minimal imbalances in the stamping process. Subsequently, the system was connected to normal production for one year. At the end of this work, we explain the anomalies detected as well as the conclusions of the article and future works. Full article
(This article belongs to the Special Issue Virtual Sensors for Industry 4.0 Era)
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17 pages, 11884 KiB  
Article
Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites
by Maurine Jacot, Victor Champaney, Francisco Chinesta and Julien Cortial
Sensors 2023, 23(4), 1946; https://doi.org/10.3390/s23041946 - 9 Feb 2023
Cited by 1 | Viewed by 1845
Abstract
This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a [...] Read more.
This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure. Full article
(This article belongs to the Special Issue Virtual Sensors for Industry 4.0 Era)
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19 pages, 9284 KiB  
Article
Incipient Wear Detection of Welding Gun Secondary Circuit by Virtual Resistance Sensor Using Mahalanobis Distance
by Daniel Ibáñez, Eduardo Garcia, Jesús Soret and Julio Martos
Sensors 2023, 23(2), 894; https://doi.org/10.3390/s23020894 - 12 Jan 2023
Cited by 3 | Viewed by 1688
Abstract
Wear of the secondary of the welding gun, caused by mechanical fatigue or due to a bad parameterization of the welding points, causes an increase in quality problems such as non-existent welds or a reduced weld nugget size. In addition to quality problems, [...] Read more.
Wear of the secondary of the welding gun, caused by mechanical fatigue or due to a bad parameterization of the welding points, causes an increase in quality problems such as non-existent welds or a reduced weld nugget size. In addition to quality problems, this defect causes production stoppages that affect the final cost of the manufactured part. Different studies have focused on evaluating the importance of different welding parameters, such as current, in the final quality of the welding nugget. However, few studies have focused on preventing weld command parameters from degrading or changing. This investigation seeks to determine the wear of the secondary circuit to avoid variability in the current supplied to the welding point caused by this defect and the increase in circuit resistance, especially in industrial environments. In this work, a virtual sensor is developed to estimate the resistance of the welding arm based on previous research, which has shown the possibility of detecting secondary wear by analysing the duty cycle of the power circuit. From the data of the virtual sensor, an anomaly detection method based on the Mahalanobis distance is developed. Finally, an integral system for detecting secondary wear of welding guns in real production lines is presented. This system establishes performance thresholds based on the analysis of the Mahalanobis distance distribution, allowing monitoring of the secondary circuit wear condition after each welding cycle. The results obtained show how the system can detect incipient wear in welding guns, regardless of which part of the secondary the wear occurs, improving decision-making and reducing quality problems. Full article
(This article belongs to the Special Issue Virtual Sensors for Industry 4.0 Era)
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Review

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27 pages, 2216 KiB  
Review
Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review
by Bin Yang, Shuang Yang, Zhihan Lv, Faming Wang and Thomas Olofsson
Sensors 2022, 22(23), 9294; https://doi.org/10.3390/s22239294 - 29 Nov 2022
Cited by 29 | Viewed by 6963
Abstract
Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial [...] Read more.
Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification. Full article
(This article belongs to the Special Issue Virtual Sensors for Industry 4.0 Era)
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