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Intelligent Control and Digital Twins for Industry 4.0
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Intelligent Control and Digital Twins for Industry 4.0

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 43754

Special Issue Editor

Dr. Aleksei Tepljakov

E-Mail Website
Guest Editor
Centre for Intelligent Systems, Department of Computer Systems, Tallinn University of Technology, Akadeemia tee 15, 12618 Tallinn, Estonia
Interests: modeling and control of complex dynamic systems; evolutionary algorithms and computational intelligence; computer vision; extended reality and digital twins; intelligent immersive virtual environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Looking at the industrial and manufacturing landscape, one finds it in a transitional state towards the so-called Industry 4.0 today. In a nutshell, Industry 4.0 follows the continued trend of digitalization of products and services on a global scale, including both industrial and consumer markets.

An important concept which forms a part of Industry 4.0 is the digital twin—a digital representation of real-life systems and phenomena. An intrinsic part of a digital twin is data. In industry, massive amounts of data can be collected through sensing technology that describe process dynamics, related process trends, etc. Digital twins allow putting these data to beneficial use through the application of advanced modeling techniques resulting in accurate process descriptions that can then be used to design intelligent control systems ensuring optimal performance and energy efficiency of industrial systems.

Some subtopics may include

- Modeling, analysis, and control of complex industrial processes;
- Applications of digital twins in Industry 4.0;

- Sensing technologies for digital twins;
- Artificial intelligence and machine-learning-based applications for industrial processes. 

Dr. Aleksei Tepljakov
Guest Editor

Manuscript Submission Information

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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

  • Industry 4.0
  • machine learning
  • artificial intelligence
  • computational intelligence
  • digital twin
  • computer vision
  • industrial application
  • intelligent control system
  • communications and signal processing
  • Internet of Things

Published Papers (17 papers)

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Editorial

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3 pages, 468 KiB  
Editorial
Intelligent Control and Digital Twins for Industry 4.0
by Aleksei Tepljakov
Sensors 2023, 23(8), 4036; https://0-doi-org.brum.beds.ac.uk/10.3390/s23084036 - 17 Apr 2023
Cited by 2 | Viewed by 1207
Abstract
One of the prominent features of the Fourth Industrial Revolution—frequently referred to as Industry 4 [...] Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)

Research

Jump to: Editorial, Review

18 pages, 6779 KiB  
Article
Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network
by Damilola Animashaun and Muhammad Hussain
Sensors 2023, 23(13), 6235; https://0-doi-org.brum.beds.ac.uk/10.3390/s23136235 - 07 Jul 2023
Cited by 1 | Viewed by 1159
Abstract
The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the [...] Read more.
The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom ‘lightweight’ convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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22 pages, 566 KiB  
Article
Open-Source Implementations of the Reactive Asset Administration Shell: A Survey
by Michael Jacoby, Michael Baumann, Tino Bischoff, Hans Mees, Jens Müller, Ljiljana Stojanovic and Friedrich Volz
Sensors 2023, 23(11), 5229; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115229 - 31 May 2023
Cited by 4 | Viewed by 2057
Abstract
The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for [...] Read more.
The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for creating Digital Twins. A structured search on GitHub and Google Scholar was conducted, leading to the selection of four implementations for detailed analysis. Objective evaluation criteria were defined, and a testing framework was created to test support for the most common AAS model elements and API calls. The results show that all implementations support at least a minimal set of required features while none implement the specification in all details, which highlights the challenges of implementing the AAS specification and the incompatibility between different implementations. This paper is therefore the first attempt at a comprehensive comparison of AAS implementations and identifies potential areas for improvement in future implementations. It also provides valuable insights for software developers and researchers in the field of AAS-based Digital Twins. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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35 pages, 13250 KiB  
Article
Educational Case Studies: Creating a Digital Twin of the Production Line in TIA Portal, Unity, and Game4Automation Framework
by Michal Balla, Oto Haffner, Erik Kučera and Ján Cigánek
Sensors 2023, 23(10), 4977; https://doi.org/10.3390/s23104977 - 22 May 2023
Cited by 5 | Viewed by 3321
Abstract
In today’s industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is [...] Read more.
In today’s industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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21 pages, 6508 KiB  
Article
Digital Twin Model of Electric Drives Empowered by EKF
by Mohsen Ebadpour, Mohammad (Behdad) Jamshidi, Jakub Talla, Hamed Hashemi-Dezaki and Zdeněk Peroutka
Sensors 2023, 23(4), 2006; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042006 - 10 Feb 2023
Cited by 11 | Viewed by 2773
Abstract
Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is [...] Read more.
Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber–physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system’s overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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29 pages, 903 KiB  
Article
Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)
by Molika Meas, Ram Machlev, Ahmet Kose, Aleksei Tepljakov, Lauri Loo, Yoash Levron, Eduard Petlenkov and Juri Belikov
Sensors 2022, 22(17), 6338; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176338 - 23 Aug 2022
Cited by 8 | Viewed by 2010
Abstract
In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, [...] Read more.
In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, such that non-AI expert end-users who require justification for the diagnosis output can easily understand the reasoning behind the decision. The method operates as follows: First, an XGBoost algorithm is used to detect and classify potential faults in the heating and cooling coil valves, sensors, and the heat recovery of an air-handling unit. Second, an XAI-based SHAP technique is used to provide explanations, with a focus on the end-users, who are HVAC engineers. Then, relevant features are chosen based on user-selected feature sets and features with high attribution scores. Finally, a sliding window system is used to visualize the short history of these relevant features and provide explanations for the diagnosed faults in the observed time period. This study aimed to provide information not only about what occurs at the time of fault appearance, but also about how the fault occurred. Finally, the resulting explanations are evaluated by seven HVAC expert engineers. The proposed approach is validated using real data collected from a shopping mall. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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22 pages, 1496 KiB  
Article
Real-Time Regulation of Beam-Based Feedback: Implementing an FPGA Solution for a Continuous Wave Linear Accelerator
by Andrei Maalberg, Michael Kuntzsch and Eduard Petlenkov
Sensors 2022, 22(16), 6236; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166236 - 19 Aug 2022
Cited by 3 | Viewed by 1437
Abstract
Control applications targeting fast industrial processes rely on real-time feasible implementations. One of such applications is the stabilization of an electron bunch arrival time in the context of a linear accelerator. In the past, only the electric field accelerating the electron bunches was [...] Read more.
Control applications targeting fast industrial processes rely on real-time feasible implementations. One of such applications is the stabilization of an electron bunch arrival time in the context of a linear accelerator. In the past, only the electric field accelerating the electron bunches was actively controlled in order to implicitly stabilize the accelerated electron beam. Nowadays, beam properties are specifically measured at a target position and then stabilized by a dedicated feedback loop acting on the accelerating structures. This dedicated loop is usually referred to as a beam-based feedback (BBF). Following this, the control system of the electron linear accelerator for beams with high brilliance and low emittance (ELBE) is planned to be upgraded by the BBF, and the problem of implementing a designed control algorithm becomes highly relevant. In this work, we propose a real-time feasible implementation of a high-order H2 regulator based on a field-programmable gate array (FPGA). By presenting simulation and synthesis results made in hardware description language (HDL) VHDL, we show that the proposed digital solution is fast enough to cover the bunch repetition rates frequently used at ELBE, such as 100 kHz. Finally, we verify the implementation by using a dedicated FPGA testbench. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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18 pages, 7107 KiB  
Article
Active Control System to Prevent Malfunctioning Caused by the Pressure Difference in Gasket Plate Heat Exchangers Applied in the Oil and Gas Industry
by Thiago Martins, Anderson Wedderhoff Spengler, Jorge Luiz Goes Oliveira, Kleber Vieira de Paiva and Laio Oriel Seman
Sensors 2022, 22(12), 4422; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124422 - 11 Jun 2022
Cited by 2 | Viewed by 1799
Abstract
In the oil and gas industry, heat exchangers are subject to loads that cause malfunctioning. These loads are divided into thermal and mechanical stresses; however, most efforts are focused on studying thermal stresses. The present work reduces mechanical stresses by mitigating pressure events [...] Read more.
In the oil and gas industry, heat exchangers are subject to loads that cause malfunctioning. These loads are divided into thermal and mechanical stresses; however, most efforts are focused on studying thermal stresses. The present work reduces mechanical stresses by mitigating pressure events in a gasket plate heat exchanger (GPHE). GPHE requires that the hot and cold stream branches have approximately the same pressure. Thus, the work focuses on controlling the pressure difference between the branches. A test bench was used to emulate, on a small scale, the typical pressure events of an oil production plant. A control valve was used in different positions to evaluate the controller. In the experiments, it was observed that the best option to control the pressure difference is to use a hydraulic pump and control valve in the flow of the controlled thermal fluid branch. The reduction in pressure events was approximately 50%. Actuator efforts are also reduced in this configuration. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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20 pages, 3355 KiB  
Article
Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications
by Vitali Vansovits, Eduard Petlenkov, Aleksei Tepljakov, Kristina Vassiljeva and Juri Belikov
Sensors 2022, 22(11), 4149; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114149 - 30 May 2022
Cited by 4 | Viewed by 1631
Abstract
In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, [...] Read more.
In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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29 pages, 1411 KiB  
Article
An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications
by Baris Baykant Alagoz, Ozlem Imik Simsek, Davut Ari, Aleksei Tepljakov, Eduard Petlenkov and Hossein Alimohammadi
Sensors 2022, 22(10), 3836; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103836 - 18 May 2022
Cited by 7 | Viewed by 2086
Abstract
Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search [...] Read more.
Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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25 pages, 1652 KiB  
Article
Performance Portrait Method: An Intelligent PID Controller Design Based on a Database of Relevant Systems Behaviors
by Mikulas Huba and Damir Vrancic
Sensors 2022, 22(10), 3753; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103753 - 14 May 2022
Cited by 9 | Viewed by 2017
Abstract
The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available [...] Read more.
The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available information extracted from a database of control tracking and disturbance rejection step responses, assessed by means of speed and shape-related performance measures of the process input and output signals, and denoted as a performance portrait (PP). In the first step, the performance portrait method (PPM) is used as a verifier, for whether the pilot analytical design of the parallel 2DoF PID controller did not omit practically interesting settings and shows that the optimality analysis can easily be extended to the series 2DoF PID controller. This is important as an explicit observer of equivalent input disturbances based on steady-state input values of ultra-local DIPDT models, while the parallel PID controller, allowing faster transient responses, needs an additional low-pass filter when reconstructed equivalent disturbances are required. Next, the design efficiency and conciseness in analyzing the effects of different loop parameters on changing the optimal processes are illustrated by an iterative use of PPM, enabled by the visualization of the dependence between the closed-loop performance and the shapes of the control signals. The main contributions of the paper are the introduction of PPM as an intelligent method for controller tuning that mimics an expert with sufficient experience to select the most appropriate solution based on a database of known solutions. In doing so, the analysis in this paper reveals new, previously undiscovered dimensions of PID control design. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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24 pages, 7332 KiB  
Article
Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems
by Viktor Makarichev, Vladimir Lukin, Oleg Illiashenko and Vyacheslav Kharchenko
Sensors 2022, 22(10), 3751; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103751 - 14 May 2022
Cited by 9 | Viewed by 2034
Abstract
Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous [...] Read more.
Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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23 pages, 1243 KiB  
Article
An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition
by Kahiomba Sonia Kiangala and Zenghui Wang
Sensors 2022, 22(3), 941; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030941 - 26 Jan 2022
Cited by 10 | Viewed by 2843
Abstract
The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. [...] Read more.
The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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27 pages, 9330 KiB  
Article
Proposal for an IIoT Device Solution According to Industry 4.0 Concept
by Andrea Vaclavova, Peter Strelec, Tibor Horak, Michal Kebisek, Pavol Tanuska and Ladislav Huraj
Sensors 2022, 22(1), 325; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010325 - 02 Jan 2022
Cited by 8 | Viewed by 4655
Abstract
Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features [...] Read more.
Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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17 pages, 2418 KiB  
Article
When Digital Twin Meets Network Softwarization in the Industrial IoT: Real-Time Requirements Case Study
by Mehdi Kherbache, Moufida Maimour and Eric Rondeau
Sensors 2021, 21(24), 8194; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248194 - 08 Dec 2021
Cited by 15 | Viewed by 3721
Abstract
The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole [...] Read more.
The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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17 pages, 1251 KiB  
Article
Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
by Olutosin Ajibola Ademola, Mairo Leier and Eduard Petlenkov
Sensors 2021, 21(22), 7529; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227529 - 12 Nov 2021
Cited by 8 | Viewed by 1804
Abstract
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. [...] Read more.
The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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Review

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55 pages, 15701 KiB  
Review
Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review
by Sylwia Werbińska-Wojciechowska and Klaudia Winiarska
Sensors 2023, 23(3), 1409; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031409 - 27 Jan 2023
Cited by 11 | Viewed by 5080
Abstract
Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature [...] Read more.
Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. Full article
(This article belongs to the Special Issue Intelligent Control and Digital Twins for Industry 4.0)
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