sensors-logo

Journal Browser

Journal Browser

Special Issue "Sense and Respond: Industrial Applications of Smart Sensors in Cyber-Physical Systems"

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

Deadline for manuscript submissions: closed (31 January 2020).

Special Issue Editors

Prof. Dr. Javier Villalba-Diez
Website SciProfiles
Guest Editor
1. Hochschule Heilbronn, Campus Schwäbisch Hall, 74081 Heilbronn, Germany
2. Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros Informáticos, Artificial Intelligence Department, 28660 Boadilla del Monte, Madrid, Spain
Interests: artificial intelligence; deep learning; cyberphysical systems; business intelligence; strategic organizational design
Special Issues and Collections in MDPI journals
Dr. Joaquin Ordieres Meré
Website SciProfiles
Guest Editor
Universidad Politécnica de Madrid, 28006 Madrid, Spain
Interests: big data analytics; IIoT; smart sensors; digital transformation of industry; artificial intelligence; machine learning; distributed computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA, from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization), and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively).

The next natural evolutionary step is to provide value by creating industrial cyber-physical assents with human-like intelligence. This will only be possible by further integrating strategic smart sensors technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards.

This Special Issue “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensors technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes. In particular, submitted papers should clearly show novel contributions and innovative applications covering- but not limited to any of the following topics:

  • Sensor networks;
  • IIoT related to production, safety, and/or health in the workplace, including pollution;
  • Industrial applications of smart sensors to (although not restricted to) cloud computing, mobile technologies, 3D printing, advanced robotics, big data, Internet of Things, RFID technology, and cognitive computing, that enable better value stream performance;
  • Applications of smart sensors that optimize the energy consumption of value creating processes and reduce the CO2 manufacturing footprint, i.e., smart grids;
  • IIoT and integration between workers and process automation to produce a more comprehensive perspective;
  • Industrial applications on cloud computing, artificial intelligence, and machine learning that enable a better value stream performance;
  • Applications of sensor networks to cyber-physical production systems;
  • Trust and accountability through DLT (distributed ledger technology) in industrial applications.

Prof. Dr. Javier Villalba Diez
Prof. Dr. Joaquín Ordieres-Meré
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 papers will be 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 2000 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

  • Smart sensors in industrial applications
  • Industrial Internet of Things
  • Cloud computing
  • 3D printing
  • Advanced robotics
  • Big data in industrial applications
  • RFID technology
  • Cognitive computing
  • Smart grid
  • Artificial intelligence in industrial applications
  • Cyber-physical production systems
  • Distributed ledger technology in industrial applications.

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects
Sensors 2020, 20(6), 1553; https://doi.org/10.3390/s20061553 - 11 Mar 2020
Cited by 1
Abstract
This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the [...] Read more.
This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria. Full article
Show Figures

Figure 1

Open AccessArticle
Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure
Sensors 2020, 20(5), 1426; https://doi.org/10.3390/s20051426 - 05 Mar 2020
Abstract
Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system [...] Read more.
Local Positioning Systems are collecting high research interest over the last few years. Its accurate application in high-demanded difficult scenarios has revealed its stability and robustness for autonomous navigation. In this paper, we develop a new sensor deployment methodology to guarantee the system availability in case of a sensor failure of a five-node Time Difference of Arrival (TDOA) localization method. We solve the ambiguity of two possible solutions in the four-sensor TDOA problem in each combination of four nodes of the system by maximizing the distance between the two possible solutions in every target possible location. In addition, we perform a Genetic Algorithm Optimization in order to find an optimized node location with a trade-off between the system behavior under failure and its normal operating condition by means of the Cramer Rao Lower Bound derivation in each possible target location. Results show that the optimization considering sensor failure enhances the average values of the convergence region size and the location accuracy by 31% and 22%, respectively, in case of some malfunction sensors regarding to the non-failure optimization, only suffering a reduction in accuracy of less than 5% under normal operating conditions. Full article
Show Figures

Graphical abstract

Open AccessArticle
Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks
Sensors 2020, 20(3), 763; https://doi.org/10.3390/s20030763 - 30 Jan 2020
Cited by 1
Abstract
In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean [...] Read more.
In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment. Full article
Show Figures

Figure 1

Open AccessArticle
Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits
Sensors 2019, 19(19), 4157; https://doi.org/10.3390/s19194157 - 25 Sep 2019
Cited by 6
Abstract
Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people’s comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps [...] Read more.
Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people’s comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain. Full article
Show Figures

Graphical abstract

Open AccessArticle
Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0
Sensors 2019, 19(18), 3987; https://doi.org/10.3390/s19183987 - 15 Sep 2019
Cited by 8
Abstract
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a [...] Read more.
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement. Full article
Show Figures

Figure 1

Open AccessArticle
Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods
Sensors 2019, 19(13), 3024; https://doi.org/10.3390/s19133024 - 09 Jul 2019
Cited by 7
Abstract
The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based [...] Read more.
The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain. Full article
Show Figures

Figure 1

Open AccessArticle
3D Tdoa Problem Solution with Four Receiving Nodes
Sensors 2019, 19(13), 2892; https://doi.org/10.3390/s19132892 - 29 Jun 2019
Cited by 5
Abstract
Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two [...] Read more.
Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s. Full article
Show Figures

Figure 1

Open AccessArticle
Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning
Sensors 2019, 19(13), 2841; https://doi.org/10.3390/s19132841 - 26 Jun 2019
Cited by 4
Abstract
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, [...] Read more.
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals. Full article
Show Figures

Figure 1

Back to TopTop