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Electronics
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Big Data and Artificial Intelligence for Industry 4.0
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Big Data and Artificial Intelligence for 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 (30 July 2022) | Viewed by 27583

Special Issue Editors

Prof. Dr. Matteo Golfarelli

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, 47522 Cesena (FC), Italy
Interests: big data; big data analytics; NoSQL databases; data warehouse; design process; data mining; analytics; social business intelligence; opinion mining; preference queries; what-if analysis; precision farming; ontologies; machine learning; trajectory data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Enrico Gallinucci

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, 47522 Cesena (FC), Italy
Interests: big data; big data analytics; NoSQL databases; social data analysis; trajectory data analysis; precision farming, business intelligence; machine learning; OLAP analysis; data warehouse; semantic web

Special Issue Information

Dear Colleagues,

Industry 4.0 pushes towards manufacturing automation through the digitalization of industrial processes and the adoption of smart technology such as Internet of Things (IoT) devices. Industry 4.0 solutions go beyond the application to smart factories, also covering the sectors of logistics and traceability, smart agriculture, healthcare, and others. The enormous amount of generated data poses challenges concerning the collection and management of big data, but also presents several opportunities, such as the extraction of knowledge from these data, which can drive decision-making and the continuous improvement of industrial operations and production chain processes. The distance between the research fields of electronics and data analysis is still considerable and makes it difficult to identify and seize these opportunities. The most common question that practitioners and researchers with electronic backgrounds ask is "What can I do with all this data?". 

This Special Issue of Electronics aims to build a bridge between the world of electronics and data analysis by presenting state-of-the-art advancements in the adoption of big data and artificial intelligence techniques to meet the requirements of Industry 4.0. We welcome novel contributions, in every application area, of innovative methodological proposals and practical applications, as well as sophisticated review articles. The topics of interest include, but are not limited to, the following: 

  • Industry 4.0;
  • industrial big data and AI;
  • cyber-physical systems;
  • smart factory;
  • predictive maintenance;
  • e-health;
  • smart agriculture;
  • industrial process monitoring and automation;
  • digital transformation processes;
  • data-driven applications

Prof. Dr. Matteo Golfarelli
Prof. Dr. Enrico Gallinucci
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

  • Industry 4.0
  • industrial big data and AI
  • cyber-physical systems
  • smart factory
  • predictive maintenance
  • e-health
  • smart agriculture
  • industrial process monitoring and automation
  • digital transformation processes
  • data-driven applications

Published Papers (5 papers)

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Research

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15 pages, 1755 KiB  
Article
ViV-Ano: Anomaly Detection and Localization Combining Vision Transformer and Variational Autoencoder in the Manufacturing Process
by Byeonggeun Choi and Jongpil Jeong
Electronics 2022, 11(15), 2306; https://doi.org/10.3390/electronics11152306 - 24 Jul 2022
Cited by 3 | Viewed by 2755
Abstract
The goal of image anomaly detection is to determine whether there is an abnormality in an image. Image anomaly detection is currently used in various fields such as medicine, intelligent information, military fields, and manufacturing. The encoder–decoder structure, which learns a normal-looking periodic [...] Read more.
The goal of image anomaly detection is to determine whether there is an abnormality in an image. Image anomaly detection is currently used in various fields such as medicine, intelligent information, military fields, and manufacturing. The encoder–decoder structure, which learns a normal-looking periodic normal pattern and shows good performance in judging anomaly scores through reconstruction errors showing the differences between the reconstructed images and the input image, is widely used in the field of anomaly detection. The existing image anomaly detection method extracts normal information through local features of the image, but the vision transformer base and the probability distribution are generated by learning the global relationship between image anomaly detection and an image patch that can locate anomalies to extract normal information. We propose Vision Transformer and VAE for Anomaly Detection (ViV-Ano), an anomaly detection model that combines a model variational autoencoder (VAE) with Vision Transformer (ViT). The proposed ViV-Ano model showed similar or better performance when compared to the existing model on a benchmark dataset. In addition, an MVTec anomaly detection dataset (MVTecAD), a dataset for industrial anomaly detection, showed similar or improved performance when compared to the existing model. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
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18 pages, 3902 KiB  
Article
Industrial Internet of Things for Condition Monitoring and Diagnosis of Dry Vacuum Pumps in Atomic Layer Deposition Equipment
by Yongho Lee, Chanyoung Kim and Sang Jeen Hong
Electronics 2022, 11(3), 375; https://doi.org/10.3390/electronics11030375 - 26 Jan 2022
Cited by 5 | Viewed by 3292
Abstract
In the modern semiconductor industry, defective products occur with unexpected small variables due to process miniaturization. Managing the condition of each part is an effective way of preventing unexpected errors. The industrial internet of things (IIoT) environment, which can monitor and analyze the [...] Read more.
In the modern semiconductor industry, defective products occur with unexpected small variables due to process miniaturization. Managing the condition of each part is an effective way of preventing unexpected errors. The industrial internet of things (IIoT) environment, which can monitor and analyze the performance degradation of parts that affect process results, enables advanced process yield management. This paper introduces the IIoT concept-based data monitoring and diagnostic system construction results. The process of pump vibration data acquisition is explained to evaluate the effectiveness of this system. The target process is deposition. The purpose of the system is to detect degradation of pumps due to by-products of the atomic layer deposition (ALD) process. The system consists of three areas: a data acquisition unit using six vibration sensors, a Web access-based monitoring unit that can monitor vibration data, and an Azure platform that searches for outliers in vibration data. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
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18 pages, 1395 KiB  
Article
Empowering Commercial Vehicles through Data-Driven Methodologies
by Paolo Bethaz, Sara Cavaglion, Sofia Cricelli, Elena Liore, Emanuele Manfredi, Stefano Salio, Andrea Regalia, Fabrizio Conicella, Salvatore Greco and Tania Cerquitelli
Electronics 2021, 10(19), 2381; https://doi.org/10.3390/electronics10192381 - 29 Sep 2021
Cited by 1 | Viewed by 2464
Abstract
In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle [...] Read more.
In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TETRAPAC, offers a generalizable approach to estimate vehicle health conditions based on monitored features enriched by innovative key performance indicators. We discussed performance of TETRAPAC in two real-life settings related to trucks. The obtained results in both tasks are promising and able to support the company’s decision-making process in the planning of maintenance interventions. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
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19 pages, 3274 KiB  
Article
Advancing Logistics 4.0 with the Implementation of a Big Data Warehouse: A Demonstration Case for the Automotive Industry
by Nuno Silva, Júlio Barros, Maribel Y. Santos, Carlos Costa, Paulo Cortez, M. Sameiro Carvalho and João N. C. Gonçalves
Electronics 2021, 10(18), 2221; https://doi.org/10.3390/electronics10182221 - 10 Sep 2021
Cited by 20 | Viewed by 7437
Abstract
The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now [...] Read more.
The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
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Review

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24 pages, 1152 KiB  
Review
Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems
by Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu and Mădălina Cocoșatu
Electronics 2021, 10(20), 2497; https://doi.org/10.3390/electronics10202497 - 14 Oct 2021
Cited by 131 | Viewed by 10125
Abstract
With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability [...] Read more.
With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Industry 4.0)
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