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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 23640
Please submit your paper and select the Journal "Energies" and the Special Issue "Measurement Applications in Industry 4.0" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the journal editor Adele Min (adele.min@mdpi.com) before submitting.

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Prof. Dr. Mauro D'Arco

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Guest Editor

Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
Interests: measurement; signal processing; control and automation; instrument development; industrial instrumentation
Special Issues, Collections and Topics in MDPI journals

Dr. Francesco Bonavolontà

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Guest Editor

Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: IoT; AR/VR-based distributed measurement systems; electrical and electronics engineering; measurement; signal processing; wireless sensor networks; embedded artificial intelligence; edge AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 identifies a new smart factory paradigm, which is based on the use of innovative digital technologies and aims at improving sustainability, efficiency, yield, and profit. A key role in Industry 4.0 is played by smart equipment, namely internet connected devices capable of transmitting information between each other and to the Cloud.

Specifically, Industry 4.0 relies on autonomous systems with a twofold character, both physical and virtual, named cyber physical systems (CPSs), which act as digital twins of the physical systems involved in the manufacturing process; internet connectivity allows setting up and updating in real-time the CPSs network. The deployment of CPSs allows gaining a holistic map of the manufacturing process, which is precious to identify leading strategies and recognize more effective resource allocation plans. For instance, thanks to the relevant computation capability of CPSs, earlier checks of the effects of exploratory actions that can help decision making become possible.

At present, both Academia and Industry are carrying out an intense research activity, which is addressed to new technologies, capable of enabling the digital transformation required by Industry 4.0 paradigm, as well as to new management strategies combining traditional know-how with Artificial Intelligent suggestions. Researchers also take into account the financial efforts required to manufactures for substituting and/or upgrading systems and machines of industrial plants; in fact, they are paying main attention to technical solutions that are affordable and can easily be deployed. In particular, low-cost modular equipment with scalability and upgradability features, that consent scheduling step-by-step transformations according to a sustainable innovation plan, are definitely recognized as principal goals.

The Special Issue will deal with all innovative research solutions that fit the above-described framework. The following list reports some non-exhaustive examples:

Human-Robot Interactions in Smart Industry

Sensor Data Fusion and IoT for Industrial Applications

Smart Training Technology

Protecting IoT Sensor Network And Industry 4.0

Smart Measurement System and Method for the Industry 4.0 environment

Electrical Systems for Industry 4.0

Wearable Sensing System for Industry 4.0 Inspection and Control

Measurement Science and Design for Additive Manufacturing

New Sensors and Applications for IoT and Industry 4.0

Thermal and Mechanical Measurement for Predictive Maintenance

Unmanned Vehicles in Industry 4.0

5G for Industry 4.0

Uncertainty in the Area of Industry 4.0

Industrial Artificial Intelligence and Machine Learning

Wireless Sensor Network for Industrial Environment

Augmented and Virtual Reality in the Smart Factory

Dr. Mauro D'Arco
Dr. Francesco Bonavolontà
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. Energies 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

Internet of Things
Instrumentation and Measurement
Control and Automation
Cyber Physical Systems
Process Modelling and Simulation
Artificial Intelligence
Machine Learning
Human-Robot interaction
Additive manufacturing
Wireless sensor networks

Published Papers (9 papers)

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Research

29 pages, 5859 KiB

Open AccessArticle

Electricity and Heat Demand in Steel Industry Technological Processes in Industry 4.0 Conditions

by Bożena Gajdzik, Radosław Wolniak and Wieslaw Wes Grebski

Energies 2023, 16(2), 787; https://doi.org/10.3390/en16020787 - 10 Jan 2023

Cited by 10 | Viewed by 2766

Abstract

The publication presents heat and electricity management in the Polish steel industry. The paper is based on actual data on heat and electricity consumption and intensity by processes in the steel industry in Poland in Industry 4.0 conditions. Two steel production processes are used in Poland: EAF Electric Arc Furnace and BOF Basic Oxygen Furnace. The analysis is an analysis of actual data is used to characterise the electricity and heat consumption by processes in the Polish steel industry. The analysis shows that the EAF technology is always more electricity intensive and the BOF technology more heat intensive. On the basis of conducted analysis, it can be concluded that pro-environmental innovations in the steel industry should first aim to reduce the electricity consumption of EAF technology and the heat consumption of BOF. An analysis of data for Poland for the period 2004–2020 shows that both cases occurred. The study shows that the heat consumption of BOF technologies has been steadily decreasing since 2010, and the electricity consumption of EAF technologies has been decreasing throughout the period under review. It can be concluded from this that the Polish steel industry is adapting to pro-environmental requirements and, through the introduction of technological innovations, is moving towards the concept of sustainable steel production according to green steel principles. The decrease in energy intensity (means electricity) of steel produced according to EAF technology is an important issue, as the high energy intensity of EAF processes affects the overall energy intensity of the steel production in Poland. In the future, the use of new innovative technological solutions, including solutions based on Industry 4.0 principles, should help the Polish steel industry to further reduce the level of electricity and heat consumption. The driving force behind the investment is the boom in the steel market. The authors made a short-term forecasts of steel production (2022–2025). The annual forecasts determined and analyses made were used to determine the heat and energy consumption of the Polish steel industry up to 2025. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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13 pages, 1946 KiB

Open AccessArticle

Building the Cognitive Enterprise in the Energy Sector

by Olga Pilipczuk

Energies 2022, 15(24), 9479; https://doi.org/10.3390/en15249479 - 14 Dec 2022

Cited by 2 | Viewed by 1160

Abstract

Currently, emerging technologies support many problems arising in the energy industry. The “cognitive enterprise” concept, introduced by the IBM company, assumes that emerging technologies are used together with cognitive workflows to increase enterprise intelligence. The pursuit of enterprises from the energy sector to obtain the status of a cognitive enterprise requires the use of many emerging technologies, including cognitive technologies. Thus, the aim of the paper was to present the current state of research and identify the core components of the cognitive enterprise. To analyze the trends and challenges in scientific research development, the bibliometric approach was used. The analysis was made by means of the Web of Science and Scopus platforms; 70,177 records were retrieved. The results comprise the geographic distribution of research and the time analysis as well as the author and affiliation analysis. Additionally, descriptive statistics are provided. Consequently, the research milestones regarding the transformation of the traditional energy enterprise into the cognitive enterprise were defined. The findings of this research have supported the construction of the conceptual framework of the core transformation components for the cognitive energy enterprise. The study have several theoretical and practical implications. The proposed framework could be used to assess the level of readiness for transformation from the traditional to the cognitive energy enterprise. The discovered scientific gaps can constitute future research directions on cognitive enterprise concept. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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10 pages, 3237 KiB

Open AccessArticle

Experimental Study of Digitizers Used in High-Precision Impedance Measurements

by Krzysztof Musioł

Energies 2022, 15(11), 4051; https://doi.org/10.3390/en15114051 - 31 May 2022

Cited by 5 | Viewed by 1269

Abstract

In the currently used primary impedance measuring systems, there is a need to compare standards with ratios different from 1:1, e.g., in order to transfer the value to multiples or submultiples of the basic quantity. Unfortunately, the commercial PXI sampling systems used to measure the voltage ratio in the impedance bridge, although they provide adequate resolution, show a considerable non-linearity of the measurement. This leads to significant error of the impedance ratio measurement. Experimental studies of commercial PXI digitizers used in primary impedance metrology are presented in the paper. The scope of the work includes presentation of the sampling measurement system hardware used in electronic synchronous impedance bridges and studies of the parameters that affect the applicability of PXI digitizers in high-precision measuring instruments. Nonlinearity errors of digitizers on boards NI PXI-4461 and NI PXI-4462 were measured and appropriate conclusions regarding possible corrections of the errors were drawn. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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17 pages, 386 KiB

Open AccessArticle

Multi-Sensor Data Fusion Approach for Kinematic Quantities

by Mauro D’Arco and Martina Guerritore

Energies 2022, 15(8), 2916; https://doi.org/10.3390/en15082916 - 15 Apr 2022

Cited by 3 | Viewed by 1756

Abstract

A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All methods defined through the framework allow the combination of signals obtained from position, velocity and acceleration sensors addressing the same target, and improvement in the observation of the kinematics of the target. Differently from several alternative methods, the considered ones need no dynamic and/or error models to operate and can be implemented with low computational burden. In fact, they gain measurements by summing filtered versions of the heterogeneous kinematic quantities. In particular, in the case of position measurement, the use of filters with finite impulse responses, all characterized by finite gain throughout the bandwidth, in place of straightforward time-integrative operators, prevents the drift that is typically produced by the offset and low-frequency noise affecting velocity and acceleration data. A simulated scenario shows that the adopted method keeps the error in a position measurement, obtained indirectly from an accelerometer affected by an offset equal to 1 ppm on the full scale, within a few ppm of the full-scale position. If the digital output of the accelerometer undergoes a second-order time integration, instead, the measurement error would theoretically rise up to

\frac{1}{2} n (n + 1)

ppm in the full scale at the n-th discrete time instant. The class of methods offered by the proposed framework is therefore interesting in those applications in which the direct position measurements are characterized by poor accuracy and one has also to look at the velocity and acceleration data to improve the tracking of a target. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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23 pages, 5985 KiB

Open AccessArticle

Predictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics

by Estelle Rossouw and Wesley Doorsamy

Energies 2021, 14(18), 5805; https://doi.org/10.3390/en14185805 - 14 Sep 2021

Cited by 5 | Viewed by 3595

Abstract

In the quest to achieve sustainable pipeline operations and improve pipeline safety, effective corrosion control and improved maintenance paradigms are required. For underground pipelines, external corrosion prevention mechanisms include either a pipeline coating or impressed current cathodic protection (ICCP). For extensive pipeline networks, time-based preventative maintenance of ICCP units can degrade the CP system’s integrity between maintenance intervals since it can result in an undetected loss of CP (forced corrosion) or excessive supply of CP (pipeline wrapping disbondment). A conformance evaluation determines the CP system effectiveness to the CP pipe potentials criteria in the NACE SP0169-2013 CP standard for steel pipelines (as per intervals specified in the 49 CFR Part 192 statute). This paper presents a predictive maintenance framework based on the core function of the ICCP system (i.e., regulating the CP pipe potential according to the NACE SP0169-2013 operating window). The framework includes modeling and predicting the ICCP unit and the downstream test post (TP) state using historical CP data and machine learning techniques (regression and classification). The results are discussed for ICCP units operating either at steady state or with stray currents. This paper also presents a method to estimate the downstream TP’s CP pipe potential based on the multiple linear regression coefficients for the supplying ICCP unit. A maintenance matrix is presented to remedy the defined ICCP unit states, and the maintenance time suggestion is evaluated using survival analysis, cycle times, and time-series trend analysis. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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22 pages, 1296 KiB

Open AccessArticle

Application of Lifecycle Measures for an Integrated Method of Environmental Sustainability Assessment of Radio Frequency Identification and Wireless Sensor Networks

by Aldona Kluczek, Bartlomiej Gladysz and Krzysztof Ejsmont

Energies 2021, 14(10), 2794; https://doi.org/10.3390/en14102794 - 13 May 2021

Cited by 3 | Viewed by 2050

Abstract

Internet of Things (IoT) technology has advanced in recent years, leading to improvements of manufacturing processes. As a result of such improvements, environmental sustainability assessments for technologies have been requested by international control agencies. Although various assessment approaches are widely applied, IoT technology requires effective assessment methods to support the decision-making process and that incorporate qualitative measures to create quantifiable values. In this paper, a new environmental sustainability assessment method is developed to assess radio frequency identification (RFID) and wireless sensors networks (WSN). This integrated assessment method incorporates a modified and redesigned conceptual methodology based on technical project evaluation (IMATOV) and an extension of conventional lifecycle measures. The results shows the most and least important metrics. The most important metrics are the categories “electronic devices disposed of completely” and “decrease in stocks”, with the greatest GWFs (20% and 19%, respectively) and IAVs (127% and 117%, respectively) and moderate consolidated degrees of fulfillment. Relatively low degrees of fulfillment are achieved by categories such as “decrease in numbers of assets”, “supply chain echelons benefiting RFID”, and “tag lifecycle duration”, with IAVs below 10%. This study promotes an integrated method to support decision-making processes in the context of environmental sustainability assessments based on lifecycle measures. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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18 pages, 6256 KiB

Open AccessArticle

Acceleration Feature Extraction of Human Body Based on Wearable Devices

by Zhenzhen Huang, Qiang Niu, Ilsun You and Giovanni Pau

Energies 2021, 14(4), 924; https://doi.org/10.3390/en14040924 - 10 Feb 2021

Cited by 5 | Viewed by 1785

Abstract

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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15 pages, 3810 KiB

Open AccessArticle

Future Sensors for Smart Objects by Printing Technologies in Industry 4.0 Scenario

by Michela Borghetti, Edoardo Cantù, Emilio Sardini and Mauro Serpelloni

Energies 2020, 13(22), 5916; https://doi.org/10.3390/en13225916 - 13 Nov 2020

Cited by 32 | Viewed by 3017

Abstract

Industry 4.0 has radically been transforming the production processes and systems with the adoption of enabling technologies, such as Internet of things (IoT), big data, additive manufacturing (AM), and cloud computing. In this context, sensors are essential to extract information about production, spare parts, equipment health, and environmental conditions necessary for improving many aspects of industrial processes (flexibility, efficiency, costs, etc.). Sensors should be placed everywhere (on machines, smart devices, objects, and tools) inside the factory to monitor in real-time physical quantities such as temperature, vibrations, deformations that could affect the production. Printed electronics (PE) offers techniques to produce unconventional sensor and systems or to make conventional objects “smart”. This work aims to analyze innovative PE technologies—inkjet printing and aerosol jet printing in combination with photonic curing—as manufacturing technologies for electronics and sensors to be integrated into objects, showing a series of sensors fabricated by PE as applications that will be adopted for smart objects and Industry 4.0. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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16 pages, 4315 KiB

Open AccessArticle

Identification of Energy Efficiency Trends in the Context of the Development of Industry 4.0 Using the Polish Steel Sector as an Example

by Radosław Wolniak, Sebastian Saniuk, Sandra Grabowska and Bożena Gajdzik

Energies 2020, 13(11), 2867; https://doi.org/10.3390/en13112867 - 04 Jun 2020

Cited by 56 | Viewed by 4631

Abstract

The steel sector is crucial for the national economy of Poland and the global economy. In response to the challenges of the global steel market and the need to increase the sector’s competitiveness, a number of actions have been taken to increase the energy efficiency of steel production. Based on the synthesis of the literature and our own research, we describe the issues related to energy efficiency and the Industry 4.0 concept. The main aim of this paper is to identify energy efficiency trends in enterprises, especially those that focus on increasing the energy efficiency of production processes, and to make recommendations for investment policy for the Polish steel sector in the era of Industry 4.0. To achieve our goals and answer the research question, we used data from 2000–2019 for the Polish steel industry. The calculations and models in this paper were made by using Gretl software. Using direct research, an econometric model was built that verified the hypothesis regarding the relationship between investment in new technologies and the energy efficiency of steel production. Future investment policies should take the implementation of Industry 4.0 tools in the steel sector into account, which, according to the authors, will measurably improve energy efficiency. Full article

(This article belongs to the Special Issue Measurement Applications in Industry 4.0)

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