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Intelligent Sensors in the Industry 4.0 and Smart Factory

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 64114

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Special Issue Editors

Dr. Susana Vieira

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Department of Mechanical Engineering, Scientific Area of Control Automation and Industrial Informatics, Instituto Superior Técnico, Universidade de Lisboa, IDMEC, 1049-001 Lisbon, Portugal
Interests: soft computing; feature selection; fuzzy modeling; optimization; fuzzy optimization; metaheuristics; computational intelligence; knowledge discovery
Special Issues, Collections and Topics in MDPI journals

Prof. João Figueiredo

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IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Centre of Mechatronics Engineering—CEM, University of Évora, 7000-671 Évora, Portugal
Interests: industrial automation and control

Prof. Dr. João Miguel da Costa Sousa

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Guest Editor

Department of Mechanical Engineering, IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Interests: computational intelligence and fuzzy systems; intelligent data analysis; smart industry; applications in energy and health care
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

While there is no single standard that could be applied to support a claim that a sensor is “smart”, Smart Sensors are typically characterized by onboard intelligence and by the ability to communicate via a digital network. With the emergence of both the Internet of Things (IoT) and the Industrial IoT (IIoT), new sensors and associated applications are emerging every day. Increasingly, these sensors are “smart”, a requirement for IoT sensors as well as for many industrial applications.

Sensors incorporated with dedicated signal processing functions are called “intelligent sensors” or “smart sensors”. This Special Issue discusses the concept of intelligent sensors in the Industry 4.0 or Smart Factory. Sensor intelligence performs a distributed signal processing in the lower layer of the sensing system hierarchy. The role of the signal processing function in intelligent sensors can be summarized as: (1) reinforcement of inherent characteristics of the sensor device, and (2) signal enhancement for the extraction of useful features of the objects.

The Role of Sensors in the Industrial IoT (IIoT)

The concept of Industry 4.0 or Smart Factory is composed of many different physical and informational subsystems, such as actuators and sensors, control systems, product management systems, and manufacturing systems that all work together. In this Special Issue, we want to take a broad and diverse view of the overall IIoT system architecture with respect to the important role of sensors and where and how they can become “intelligent”.

To ensure the high level of automation required in today’s industrial applications, equipment must be more efficient, intelligent, aware of context, and more connected; it must also be more robust and ensure greater safety for the humans interacting with them. With the advent of digital technologies, industry developed appropriate standards for process control and digital, or “smart” sensor networks, both wired and wireless. These enabled a rich variety of device statuses, IDs, diagnostics, time stamps, and other data to be communicated along with the digital measurement signal. The Industrial IoT also offers the promise of utilizing previously stranded asset data from low-cost, internet-connected smart sensors mounted directly on equipment and machines in conjunction with advanced analytics software to detect, identify, and avoid performance degradation or asset failures. Ultimately, this could optimize industrial asset availability, utilization, and performance to improve return on assets and support operational excellence.

Today, sensors are fundamental in industrial automation, but turning sensors into IIoT systems can be a major challenge. Smart devices in a factory setting are able to independently manage manufacturing processes. Sensor data, in all aspects of a manufacturing environment, are the source key of critical information. This information is then routed to a higher-level decision-making system.

The world is abuzz with applications of machine learning in almost every field—commerce, transportation, banking, or healthcare. These breakthroughs are due to rediscovered algorithms, powerful computers to run them, and most importantly, the availability of bigger and better data to train the algorithms. In this Special Issue, entitled “Intelligent Sensors”, we want to look at a few of the latest applications using intelligent sensors in which the system architecture examples follow the IIoT approach for the Industry 4.0.

We invite the research community to submit original research papers in the following topics:

Intelligent automation
Soft sensors
Industrial Internet of Things
Identification/processes/industry
Predictive maintenance
Other applications

Prof. Susana Vieira
Prof. João Figueiredo
Prof. João Miguel da Costa Sousa
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.

Published Papers (6 papers)

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Research

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15 pages, 1065 KiB

Open AccessArticle

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

by Pieter Moens, Vincent Bracke, Colin Soete, Sander Vanden Hautte, Diego Nieves Avendano, Ted Ooijevaar, Steven Devos, Bruno Volckaert and Sofie Van Hoecke

Sensors 2020, 20(15), 4308; https://doi.org/10.3390/s20154308 - 02 Aug 2020

Cited by 34 | Viewed by 5057

Abstract

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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22 pages, 7398 KiB

Open AccessArticle

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

by Chengjun Chen, Kai Huang, Dongnian Li, Zhengxu Zhao and Jun Hong

Sensors 2020, 20(15), 4213; https://doi.org/10.3390/s20154213 - 29 Jul 2020

Cited by 6 | Viewed by 2538

Abstract

The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function of torque measurement is still an extensively used assembly tool. Therefore, wrench torque monitoring is one of the urgent problems that needs to be solved. This study proposes a multi-segmentation parallel convolution neural network (MSP-CNN) model for estimating assembly torque using surface electromyography (sEMG) signals, which is a method of torque monitoring through classification methods. The MSP-CNN model contains two independent CNN models with different or offset torque granularities, and their outputs are fused to obtain a finer classification granularity, thus improving the accuracy of torque estimation. First, a bolt tightening test bench is established to collect sEMG signals and tightening torque signals generated when the operator tightens various bolts using a wrench. Second, the sEMG and torque signals are preprocessed to generate the sEMG signal graphs. The range of the torque transducer is divided into several equal subdivision ranges according to different or offset granularities, and each subdivision range is used as a torque label for each torque signal. Then, the training set, verification set, and test set are established for torque monitoring to train the MSP-CNN model. The effects of different signal preprocessing methods, torque subdivision granularities, and pooling methods on the recognition accuracy and torque monitoring accuracy of a single CNN network are compared experimentally. The results show that compared to maximum pooling, average pooling can improve the accuracy of CNN torque classification and recognition. Moreover, the MSP-CNN model can improve the accuracy of torque monitoring as well as solve the problems of non-convergence and slow convergence of independent CNN network models. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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25 pages, 784 KiB

Open AccessArticle

Speeding Up the Implementation of Industry 4.0 with Management Tools: Empirical Investigations in Manufacturing Organizations

by Rok Črešnar, Vojko Potočan and Zlatko Nedelko

Sensors 2020, 20(12), 3469; https://doi.org/10.3390/s20123469 - 19 Jun 2020

Cited by 34 | Viewed by 5569

Abstract

The main purpose of this study is to examine how the use of management tools supports the readiness of manufacturing organizations for the implementation of Industry 4.0. The originality of the research is reflected in the exploration of the relationship between the use of the selected well-known management tools and their readiness for the implementation of Industry 4.0, which was assessed using a combination of two models—one developed by the National Academy of Science and Engineering (Acatech) and the other by the University of Warwick. The relationship was assessed by applying structural equation modeling techniques to a data set of 323 responses from employees in manufacturing organizations. The results show that the use of six sigma, total quality management, radio frequency identification, a balanced scorecard, rapid prototyping, customer segmentation, mission and vision statements, and digital transformation is positively associated with Industry 4.0 readiness. Inversely, outsourcing and strategic planning are negatively associated with Industry 4.0 readiness, while lean manufacturing, which is often emphasized as the cornerstone of Industry 4.0 implementation, is not associated with Industry 4.0 readiness in our study. These findings can help organizations to understand how to consider and measure readiness for the implementation of Industry 4.0 more comprehensively and present guidelines on how the use of management tools in manufacturing organizations can foster their implementation of Industry 4.0 principles. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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13 pages, 19973 KiB

Open AccessArticle

Wearable Augmented Reality Platform for Aiding Complex 3D Trajectory Tracing

by Sara Condino, Benish Fida, Marina Carbone, Laura Cercenelli, Giovanni Badiali, Vincenzo Ferrari and Fabrizio Cutolo

Sensors 2020, 20(6), 1612; https://doi.org/10.3390/s20061612 - 13 Mar 2020

Cited by 32 | Viewed by 5659

Abstract

Augmented reality (AR) Head-Mounted Displays (HMDs) are emerging as the most efficient output medium to support manual tasks performed under direct vision. Despite that, technological and human-factor limitations still hinder their routine use for aiding high-precision manual tasks in the peripersonal space. To overcome such limitations, in this work, we show the results of a user study aimed to validate qualitatively and quantitatively a recently developed AR platform specifically conceived for guiding complex 3D trajectory tracing tasks. The AR platform comprises a new-concept AR video see-through (VST) HMD and a dedicated software framework for the effective deployment of the AR application. In the experiments, the subjects were asked to perform 3D trajectory tracing tasks on 3D-printed replica of planar structures or more elaborated bony anatomies. The accuracy of the trajectories traced by the subjects was evaluated by using templates designed ad hoc to match the surface of the phantoms. The quantitative results suggest that the AR platform could be used to guide high-precision tasks: on average more than 94% of the traced trajectories stayed within an error margin lower than 1 mm. The results confirm that the proposed AR platform will boost the profitable adoption of AR HMDs to guide high precision manual tasks in the peripersonal space. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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40 pages, 2884 KiB

Open AccessEditor’s ChoiceReview

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

by Martin Pech, Jaroslav Vrchota and Jiří Bednář

Sensors 2021, 21(4), 1470; https://doi.org/10.3390/s21041470 - 20 Feb 2021

Cited by 158 | Viewed by 27043

Abstract

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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34 pages, 1069 KiB

Open AccessReview

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

by Angelos Angelopoulos, Emmanouel T. Michailidis, Nikolaos Nomikos, Panagiotis Trakadas, Antonis Hatziefremidis, Stamatis Voliotis and Theodore Zahariadis

Sensors 2020, 20(1), 109; https://doi.org/10.3390/s20010109 - 23 Dec 2019

Cited by 186 | Viewed by 16082

Abstract

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research. Full article

(This article belongs to the Special Issue Intelligent Sensors in the Industry 4.0 and Smart Factory)

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