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Electronics
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Intelligent Manufacturing Systems and Applications in Industry 4.0
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Intelligent Manufacturing Systems and Applications in Industry 4.0

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 15539

Special Issue Editors

Prof. Dr. Chunjie Yang

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Guest Editor
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: industrial process modeling, control and fault diagnosis; green intelligent manufacturing technology and implementation; metallurgical process energy saving and emission reduction advanced control and optimization; industrial process optimization and monitoring software system design and development
Prof. Dr. Qing-Guo Wang

E-Mail Website
Guest Editor
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087, China
Interests: Industry 4.0; various industrial and environmental processes; energy systems; defense systems; medical technology and financial engineering; prediction; control
Prof. Dr. Ping Zhou

E-Mail Website
Guest Editor
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
Interests: operational feedback/optimization control for intelligent manufacturing; data-driven modeling, control and optimization
Prof. Dr. Zhaohui Jiang

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: detection technology and automatic equipment; image processing; industrial virtual reality (VR); modeling and optimal control of complex industrial processes
Dr. Zhuoyun Nie

E-Mail Website
Guest Editor
Associate Professor, School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
Interests: industrial process control and applications; complex systems; mobile wheeled robot and motion control; disturbance rejection control; intelligent optimization; advanced control techniques and implementation

Special Issue Information

Dear Colleagues,

Intelligent manufacturing system (IMS) integrates smart machines, new-generation information and communications technology and artificial intelligence (AI) for the best possible manufacturing outcomes. It is regarded as a new manufacturing model that greatly upgrades the design, production, management, and integration of the whole life cycle of a typical product. It plays the core role in industry 4.0. Recently, intelligent manufacturing has been receiving much more attention from academia and industry. New technologies such as deep reinforcement learning (DRL), knowledge automation (KA), computer vision (CV), the internet of things (IoT) and cyber-physical system (CPS), enable the manufacturing system more intelligent and smarter:

  • AI plays an essential role in an IMS by providing typical features such as learning, reasoning, and acting. With the use of DRL technology, manufacturing systems are trained beforehand to cooperate and aim for a near-optimal schedule.
  • IMS varies with various factors, such as operating environment, external uncertainties, and self-operating conditions. This requires a comprehensive model obtained by fusing expert knowledge and visual image information.
  • IoT-enabled manufacturing refers to an advanced principle in which typical production resources are converted into smart manufacturing objects (SMOs) that can sense, interconnect, and interact with each other to automatically and adaptively carry out manufacturing logics. 
  • A CPS involves a large number of trans-disciplinary methodologies such as cybernetics theory, mechanical engineering and mechatronics, design and process science, manufacturing systems, and computer science. One of the key technical methods is embedded systems, which enable a highly coordinated and combined relationship between physical objects and their computational elements or services.

This Special Issue aims to provide a platform for researchers and engineers from academic institutes and industrial sectors to exchange their research ideas, disseminate their recent research results, and overview emerging research directions in intelligent manufacturing systems and applications.

Potential topics include, but are not limited to, the following:

  • Artificial intelligence techniques for manufacturing systems;
  • Control methods and strategies in manufacturing systems;
  • Intelligent optimization for manufacturing systems;
  • Cloud manufacturing in the context of Industry 4.0;
  • Internet of Things (IoT)-enabled manufacturing;
  • Digital twins for manufacturing systems;
  • Cyber-physical systems;
  • Intelligent design for customized products;
  • Integrated and intelligent manufacturing;
  • Industrial internet architecture and theory;
  • Information systems and network security;
  • Industrial robots.

Prof. Dr. Chunjie Yang
Prof. Dr. Qing-Guo Wang
Prof. Dr. Ping Zhou
Prof. Dr. Zhaohui Jiang
Dr. Zhuoyun Nie
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. Electronics 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 2400 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

  • cyber–physical systems
  • digital twins
  • discrete event systems
  • intelligent control
  • internet of things (IoT)
  • cloud computing (CC)
  • big data analytics (BDA)
  • artificial intelligence (AI)
  • knowledge engineering
  • industrial robots
  • network security

Published Papers (6 papers)

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Research

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17 pages, 16590 KiB  
Article
Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example
by Xiaoke Huang and Chunjie Yang
Electronics 2024, 13(5), 845; https://doi.org/10.3390/electronics13050845 - 22 Feb 2024
Viewed by 516
Abstract
Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to [...] Read more.
Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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17 pages, 6140 KiB  
Article
Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks
by Stamatis Apeiranthitis, Paraskevi Zacharia, Avraam Chatzopoulos and Michail Papoutsidakis
Electronics 2024, 13(2), 460; https://doi.org/10.3390/electronics13020460 - 22 Jan 2024
Cited by 1 | Viewed by 1127
Abstract
All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a [...] Read more.
All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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20 pages, 10649 KiB  
Article
Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process
by Ke Wang, Ping Wu, Siwei Lou, Haipeng Pan and Jinfeng Gao
Electronics 2023, 12(23), 4773; https://doi.org/10.3390/electronics12234773 - 24 Nov 2023
Viewed by 641
Abstract
Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning [...] Read more.
Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. This paper proposes fused local manifold learning (FLML), which synthesizes the typical manifold learning methods to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for process data dimension reduction. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process whose average MDR and average FAR of FLML T2 are 7.58% and 0.21% and a real blast furnace ironmaking process whose MDR and FAR of FLML T2 are 2.80% and 0.00% are carried out to demonstrate the superiority and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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16 pages, 4831 KiB  
Article
Smith-Predictor-Based Design of Analytical PI-PD Control for Series Cascade Processes with Time Delay
by Mingjie Li, Minyuan Xin, Zhicheng Zhao, Jianan Wang and Xiao Hu
Electronics 2023, 12(19), 4089; https://doi.org/10.3390/electronics12194089 - 29 Sep 2023
Viewed by 567
Abstract
For series cascade processes with a time delay, such as the first-order with time-delay (FOTD) process, the integral with time-delay (ITD) process, second-order integral with time-delay (SOITD) process, and unstable first-order with time-delay (UFOTD) process, this paper proposes a Smith-predictor-based design of analytical [...] Read more.
For series cascade processes with a time delay, such as the first-order with time-delay (FOTD) process, the integral with time-delay (ITD) process, second-order integral with time-delay (SOITD) process, and unstable first-order with time-delay (UFOTD) process, this paper proposes a Smith-predictor-based design of analytical PI-PD control for these series cascade processes with a time delay. Firstly, targeting the common FOTD model in the secondary loop, a controller with a PID structure is designed using the direct synthesis method. Additionally, a Smith-predictor-based PI-PD control structure is adopted for the prevalent process model in the primary loop. Then, expressions representing the relationship between the corresponding controller parameters and the maximum sensitivity (Ms) index are established to facilitate the analytical design of the controllers. Finally, based on simulation experiments, the effectiveness and superiority are validated by using the proposed method. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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21 pages, 4374 KiB  
Article
Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments
by Sungju Lee and Taikyeong Jeong
Electronics 2022, 11(23), 4037; https://doi.org/10.3390/electronics11234037 - 05 Dec 2022
Cited by 1 | Viewed by 1727
Abstract
The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose [...] Read more.
The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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Review

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28 pages, 1790 KiB  
Review
Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends
by Zhang Wenhua, Faizan Qamar, Taj-Aldeen Naser Abdali, Rosilah Hassan, Syed Talib Abbas Jafri and Quang Ngoc Nguyen
Electronics 2023, 12(3), 546; https://doi.org/10.3390/electronics12030546 - 20 Jan 2023
Cited by 39 | Viewed by 10156
Abstract
Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different [...] Read more.
Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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