Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.8
2.3
Journals
Systems
Special Issues
Data-Driven Analysis of Industrial Systems Using AI
share announcement

Data-Driven Analysis of Industrial Systems Using AI

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1037

Special Issue Editors

Dr. Tai-Woo Chang

E-Mail Website
Guest Editor
Department of Industrial & Management Engineering/Intelligence & Manufacturing Research Center, Kyonggi University, Suwon 16227, Republic of Korea
Interests: industrial artificial intelligence; smart factory; smart logistics; supply chain management
Dr. Gyusun Hwang

E-Mail Website
Guest Editor
School of Industrial Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: simulation; digital twin; smart factory; key performance indicator

Special Issue Information

Dear Colleagues,

As automation devices and information systems are increasingly implemented across various industrial sites, large volumes of data are being generated, making it routine to analyze and apply this data using artificial intelligence (AI). With extensive research being conducted on data sharing among industrial systems, data collection and analysis from these systems, and the utilization of the analyzed results, we aim to contribute by sharing exceptional research findings in academic papers. This collection will also include case studies of data analysis and application across diverse sectors, encompassing not only the manufacturing industry but also various service industries such as the logistics industry.

This Special Issue invites papers with scientific contributions that propose innovative and original approaches that have been or can be applied in industry. We hope this issue will provide an opportunity for academics and practitioners to share their theoretical and practical knowledge and findings in the field.

In particular, this issue aims to present a collection of state-of-the-art solutions to the different types of data-driven analysis using AI, such as machine learning, data mining, process mining, and optimization techniques. Potential topics include, but are not limited to, the following:

  • Data analytics for industrial systems;
  • Data-driven analysis in manufacturing and related field;
  • Data-driven platform or data spaces;
  • Data-driven product service systems (PSS);
  • AI in industry;
  • Theory and methods of big data analysis and advanced analytics;
  • Case studies on data-driven analysis of industrial systems;
  • State-of-the-art review of data analytics in specific industries.

Dr. Tai-Woo Chang
Dr. Gyusun Hwang
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. Systems is an international peer-reviewed open access monthly 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

  • data analytics
  • industrial systems
  • artificial intelligence
  • machine learning
  • process mining
  • big data analysis
  • data-driven platforms
  • product-service systems (PSS)

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 2502 KiB  
Article
Fault Detection and Diagnosis in Air-Handling Unit (AHU) Using Improved Hybrid 1D Convolutional Neural Network
by Prince, Byungun Yoon and Prashant Kumar
Systems 2025, 13(5), 330; https://doi.org/10.3390/systems13050330 (registering DOI) - 1 May 2025
Abstract
The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs is essential for maintaining continuous HVAC operation and preventing system breakdowns. The advent of artificial intelligence has transformed the AHU fault diagnosis [...] Read more.
The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs is essential for maintaining continuous HVAC operation and preventing system breakdowns. The advent of artificial intelligence has transformed the AHU fault diagnosis techniques. Specifically, deep learning has obviated the necessity for manual feature extraction and selection, thereby streamlining the fault diagnosis process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining a CNN model with a long short-term memory (LSTM) model to diagnose the faults in AHUs. The advantages of the LSTM model and convolutional layers are combined to identify significant patterns in the input data, which considerably facilitates the detection of AHU defects. The hybrid design enhances the network’s capability to capture both local and global characteristics, thus improving its ability to differentiate between normal and abnormal circumstances. The proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity to nuanced fault patterns. Furthermore, its efficacy is corroborated through comparisons with state-of-the-art AHU fault identification techniques. Full article
(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)
►▼ Show Figures

Figure 1

26 pages, 5652 KiB  
Article
Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining
by Yutika Amelia Effendi and Minsoo Kim
Systems 2025, 13(4), 229; https://doi.org/10.3390/systems13040229 - 27 Mar 2025
Viewed by 260
Abstract
Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper [...] Read more.
Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper introduces a Timed Genetic-Inductive Process Mining (TGIPM) algorithm, a novel approach that integrates the strengths of Timed Genetic Process Mining (TGPM) and Inductive Mining (IM). TGPM extends traditional Genetic Process Mining (GPM) by incorporating time-based analysis, while the IM is widely recognized for producing sound and precise process models. For the first time, these two algorithms are combined into a unified framework to address both missing activity recovery and structural correctness in process discovery. This study evaluates two scenarios: a sequential approach, in which TGPM and IM are executed independently and sequentially, and the TGIPM approach, where both algorithms are integrated into a unified framework. Experimental results using real-world event logs from a health service in Indonesia demonstrate that TGIPM achieves higher fitness, precision, and generalization compared to the sequential approach, while slightly compromising simplicity. Moreover, the TGIPM algorithm exhibits lower computational cost and more effectively captures parallelism, making it particularly suitable for large and incomplete datasets. This research underscores the potential of TGIPM to enhance process mining outcomes, offering a robust framework for accurate and efficient process discovery while driving process innovation across industries. Full article
(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)
►▼ Show Figures

Figure 1

Review

Jump to: Research

30 pages, 2663 KiB  
Review
Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling
by Woojun Jung, Insung Hwang and Keuntae Cho
Systems 2025, 13(4), 253; https://doi.org/10.3390/systems13040253 - 3 Apr 2025
Viewed by 386
Abstract
A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the [...] Read more.
A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the relational structure between authors to identify key contributors, and extracts major themes from extensive texts to highlight essential research content. To achieve these objectives, trend analysis, network analysis, and topic modeling were conducted on 352 research papers collected from the Web of Science between 2004 and 2023. To ensure the validity of the topic modeling results, a topic correlation matrix was also performed. The results revealed three key findings. First, SDL research has surged since 2019, driven by advancements in AI technologies, reflecting heightened activity in this field. Second, modern scientific research is advancing with a focus on data-driven approaches, artificial intelligence applications, and experimental optimization through the utilization of SDLs. Third, SDL research exhibits interdisciplinary convergence, encompassing material optimization, biological processes, and AI predictive algorithms. This study underscores the growing importance of SDLs as a research tool across diverse academic disciplines and provides practical insights into sustainable future scientific research directions and strategic approaches. Full article
(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop