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Virtual Sensors for Industry 4.0 Era

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9670

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Special Issue Editors

Prof. Dr. Nicolás Montés

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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

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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

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Keywords

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

Published Papers (4 papers)

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Research

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17 pages, 1312 KiB

Open AccessArticle

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 1 | Viewed by 1003

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 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

Open AccessArticle

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 - 09 Feb 2023

Cited by 1 | Viewed by 1223

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 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

Open AccessArticle

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 2 | Viewed by 1244

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, 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

Open AccessReview

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 13 | Viewed by 5484

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 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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