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Data-Driven Analysis of Industrial Systems Using AI

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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: closed (30 September 2025) | Viewed by 12283

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Special Issue Editors

Dr. Tai-Woo Chang

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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

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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

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Keywords

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

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Published Papers (11 papers)

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Research

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38 pages, 14320 KB

Open AccessArticle

Naval AI-Based Utility for Remaining Useful Life Prediction and Anomaly Detection for Lifecycle Management

by Carlos E. Pardo B., Oscar I. Iglesias R., Maicol D. León A., Christian G. Quintero M., Miguel Andrés Garnica López and Andrés Ricardo Pedraza Leguizamón

Systems 2025, 13(10), 845; https://doi.org/10.3390/systems13100845 - 26 Sep 2025

Viewed by 403

Abstract

This work presents the development of an intelligent system designed to support the predictive maintenance of the Colombian Navy’s maritime vessels through the estimation of remaining useful life and unsupervised anomaly detection, within the framework of the project called “Colombian Integrated Platform Supervision and Control System” (SISCP-C). This project seeks to guarantee the sustainability of the vessels over time, increase their operational availability, and optimize their life cycle cost, in accordance with the institution’s strategic direction established in the Naval Development Plan 2042. The system provides useful information to the crew, enabling informed decision-making for intelligent and efficient maintenance strategies. To address the limited availability of normal operating data, synthetic data generation techniques with seeding are implemented, including tabular variational autoencoders, conditional tabular generative adversarial networks, and Gaussian copulas. Among these, tabular variational autoencoders achieved the best performance and are used to generate synthetic datasets under normal conditions for the Wärtsilä 6L26 diesel engine (manufactured by Wärtsilä Italia S.p.A., Trieste, Italy). These datasets are used to train several unsupervised anomaly detection models, including one-class support vector machines, classical autoencoders, and long short-term memory-based autoencoders. The long short-term memory autoencoders outperformed the others in terms of detection metrics. Dedicated multivariate long short-term memory autoencoders are subsequently trained for each engine subsystem. By calculating the mean absolute error of the reconstructions, a subsystem-specific health index is computed, which is used to estimate the remaining useful life. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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30 pages, 1434 KB

Open AccessArticle

Conditional Entropy-Based Sequential Decision-Making for AI Adoption in Manufacturing: A Reinforcement Learning Approach

by Ga-hyun Lee, Byunghun Song and Hyun-woo Jeon

Systems 2025, 13(9), 830; https://doi.org/10.3390/systems13090830 - 21 Sep 2025

Viewed by 616

Abstract

Most small- and medium-sized manufacturers face challenges in adopting artificial intelligence (AI) in production systems due to limited domain expertise and challenges in making interrelated decisions. This decision-making process can be characterized as sequential decision-making (SDM), in which guidance on the decision order is valuable. This study proposes a data-driven SDM framework to identify an effective order of key decision elements for AI adoption, aiming to rapidly reduce uncertainty at each decision stage. The framework employs a Q-learning-based reinforcement learning approach, using conditional entropy as the reward function to quantify uncertainty. Based on a review of 55 studies applying AI to milling processes, the proposed model identifies the following decision order that minimizes cumulative uncertainty: sensor, data collection interval, data dimension, AI technique, data type, and data collection period. To validate the model, we conduct simulations of 4000 SDM episodes under rule-based constraints using the number of corrected episodes as a performance metric. Simulation results show that the proposed model generates decision orders with no corrections and that knowing the relative order between two elements is more effective than knowing exact positions. The proposed data-driven framework is broadly applicable and can be extended to AI adoption in other manufacturing domains. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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24 pages, 8898 KB

Open AccessArticle

Performance and Efficiency Gains of NPU-Based Servers over GPUs for AI Model Inference

by Youngpyo Hong and Dongsoo Kim

Systems 2025, 13(9), 797; https://doi.org/10.3390/systems13090797 - 11 Sep 2025

Viewed by 1816

Abstract

The exponential growth of AI applications has intensified the demand for efficient inference hardware capable of delivering low-latency, high-throughput, and energy-efficient performance. This study presents a systematic, empirical comparison of GPU- and NPU-based server platforms across key AI inference domains: text-to-text, text-to-image, multimodal understanding, and object detection. We configure representative models—LLama-family for text generation, Stable Diffusion variants for image synthesis, LLaVA-NeXT for multimodal tasks, and YOLO11 series for object detection—on a dual NVIDIA A100 GPU server and an eight-chip RBLN-CA12 NPU server. Performance metrics including latency, throughput, power consumption, and energy efficiency are measured under realistic workloads. Results demonstrate that NPUs match or exceed GPU throughput in many inference scenarios while consuming 35–70% less power. Moreover, optimization with the vLLM library on NPUs nearly doubles the tokens-per-second and yields a 92% increase in power efficiency. Our findings validate the potential of NPU-based inference architectures to reduce operational costs and energy footprints, offering a viable alternative to the prevailing GPU-dominated paradigm. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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26 pages, 11307 KB

Open AccessArticle

Fault Detection and Diagnosis of Rolling Bearings in Automated Container Terminals Using Time–Frequency Domain Filters and CNN-KAN

by Taoying Li, Ruiheng Cheng and Zhiyu Dong

Systems 2025, 13(9), 796; https://doi.org/10.3390/systems13090796 - 10 Sep 2025

Viewed by 464

Abstract

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production efficiency, reduce the safety risks, and achieve sustainable development of equipment in ACTs. However, existing rolling-bearing diagnosis models are vulnerable to environmental noise and interference, depressing accuracy and raising misclassification, and they seldom achieve both noise robustness and a lightweight design; robustness usually increases complexity, while compact networks degrade under low signal-to-noise ratios. Therefore, this paper proposes a noise-robust, lightweight, and interpretable deep learning framework for fault detection and diagnosis of rolling bearings in automated container terminal (ACT) equipment. The framework comprises four coordinated components, including Time-Domain Filter, Frequency-Domain Filter, Physical-Feature Extraction module, and Classification module, whose joint optimization yields complementary time–frequency representations and physics-aligned features, and fuses into robust diagnostic decisions under noisy and non-stationary environments. The first component highlights impulsive transients, the second component emphasizes harmonic and sideband modulation, the third module introduces two differentiable and rolling bearing-signal-informed objectives to align learning with characteristic bearing signatures by weighted-average kurtosis and an Lp/Lq-based envelope-spectral concentration index, and the last module integrates multi-layer convolutional neural networks (CNN) and Deep Kolmogorov–Arnold Networks (DeepKAN). Finally, two public datasets are employed to estimate the model’s performance, and results indicate that the proposed method outperforms others. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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19 pages, 1190 KB

Open AccessArticle

A Lightweight AI System to Generate Headline Messages for Inventory Status Summarization

by Bongjun Ji, Yukwan Hwang, Donghun Kim, Jungmin Park, Minhyeok Ryu and Yongkyu Cho

Systems 2025, 13(9), 741; https://doi.org/10.3390/systems13090741 - 26 Aug 2025

Viewed by 658

Abstract

In the manufacturing supply chain, management reports often begin with concise messages that summarize key inventory insights. Traditionally, human analysts manually crafted these summary messages by sifting through complex data—a process that is both time-consuming and prone to inconsistency. In this research study, we present an AI-based system that automatically generates high-quality inventory insight summaries, referred to as “headline messages,” using real-world inventory data. The proposed system leverages lightweight natural language processing (NLP) and machine learning models to achieve accurate and efficient performance. Historical messages are first clustered using a sentence-translation MiniLM model that provides fast semantic embedding. This is used to derive key message categories and define structured input features for this purpose. Then, an explainable and low-complexity classifier trained to predict appropriate headline messages based on current inventory metrics using minimal computational resources. Through empirical experiments with real enterprise data, we demonstrate that this approach can reproduce expert-written headline messages with high accuracy while reducing report generation time from hours to minutes. This study makes three contributions. First, it introduces a lightweight approach that transforms inventory data into concise messages. Second, the proposed approach mitigates confusion by maintaining interpretability and fact-based control, and aligns wording with domain-specific terminology. Furthermore, it reports an industrial validation and deployment case study, demonstrating that the system can be integrated with enterprise data pipelines to generate large-scale weekly reports. These results demonstrate the application and technological innovation of combining small-scale language models with interpretable machine learning to provide insights. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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24 pages, 4001 KB

Open AccessArticle

Manufacturing Readiness Assessment Technique for Defense Systems Development Using a Cybersecurity Evaluation Method

by Si-Il Sung and Dohoon Kim

Systems 2025, 13(9), 738; https://doi.org/10.3390/systems13090738 - 25 Aug 2025

Viewed by 535

Abstract

Weapon systems have transitioned from hardware-centered designs to software-driven platforms, introducing new cybersecurity risks, including software manipulation and cyberattacks. To address these challenges, this study proposes an improved manufacturing readiness level assessment (MRLA) method that integrates cybersecurity capabilities into the evaluation process to address the gaps in hardware-focused practices in South Korea. Based on the MITRE adversarial tactics, techniques, and common knowledge, and the defensive cybersecurity framework, this study identified security requirements, assessed vulnerabilities, and constructed exploratory testing scenarios using defense trees. These methods evaluate system resilience, the effectiveness of security controls, and response capabilities under diverse attack scenarios. The proposed MRLA approach incorporates cyberattacks and defense scenarios that may occur in operational environments. This approach was validated through a case study involving unmanned vehicle systems, where the modified MRLA successfully identified and mitigated critical cybersecurity threats. Consequently, the target operational mode summary/mission profile of a weapon system can be revised based on practical considerations, enhancing the cybersecurity assessments and thereby improving the operational readiness of weapon systems through scenario-based, realistic evaluation frameworks. The findings of this study demonstrate the practical utility of incorporating cybersecurity evaluations into MRLA, contributing to more resilient defense systems. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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27 pages, 9163 KB

Open AccessArticle

Meta-Learning-Based LSTM-Autoencoder for Low-Data Anomaly Detection in Retrofitted CNC Machine Using Multi-Machine Datasets

by Ji-Min Woo, Seong-Hyeon Ju, Jin-Hyeon Sung and Kyung-Min Seo

Systems 2025, 13(7), 534; https://doi.org/10.3390/systems13070534 - 1 Jul 2025

Cited by 1 | Viewed by 1073

Abstract

In recent manufacturing environments, the use of digitally retrofitted equipment has grown substantially, yet this trend also amplifies the challenge of ensuring stable operation through effective anomaly detection. Retrofitted systems suffer from two critical obstacles: a severe scarcity of labeled data and substantial variability in operational patterns across machines and products. To overcome these issues, this study introduces a novel anomaly detection framework that integrates Model-Agnostic Meta-Learning (MAML) with a Long Short-Term Memory Autoencoder (LSTM-Autoencoder) under a multi-machine-based task formulation. By constructing meta-tasks from time-series datasets collected on multiple five-axis computer numerical control (CNC) machines, our method enables rapid adaptation to unseen machines and production scenarios with only a few training examples. The experimental results demonstrate that, even under data-scarce conditions, the proposed model achieves an accuracy of 98.02% and an F1-score of 94.74%, representing improvements of 4.2 percentage points in accuracy and 16.9 percentage points in F1-score over conventional transfer learning approaches. Furthermore, in cross-validation on entirely new machine data, our framework outperforms existing models by 18.1% in accuracy, evidencing superior generalization capability. These findings suggest that the proposed multi-machine-based Model-Agnostic Meta-Learning Long Short-Term Memory Autoencoder (MAML LSTM-Autoencoder) can significantly enhance operational efficiency and reduce maintenance costs in retrofitted manufacturing equipment, thereby improving overall productivity and paving the way for real-time industrial deployment. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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16 pages, 984 KB

Open AccessArticle

Reinforcement Learning Model for Optimizing Bid Price and Service Quality in Crowdshipping

by Daiki Min, Seokgi Lee and Yuncheol Kang

Systems 2025, 13(6), 440; https://doi.org/10.3390/systems13060440 - 5 Jun 2025

Viewed by 882

Abstract

Crowdshipping establishes a short-term connection between shippers and individual carriers, bridging the service requirements in last-mile logistics. From the perspective of a carrier operating multiple vehicles, this study considers the challenge of maximizing profits by optimizing bid strategies for delivery prices and transportation conditions in the context of bid-based crowdshipping services. We considered two types of bid strategies: a price bid that adjusts the RFQ freight charge and a multi-attribute bid that scores both price and service quality. We formulated the problem as a Markov decision process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, given the complexity of the newly proposed problem, which involves multiple vehicles, route optimizations, and multiple attributes of bids, we employed a reinforcement learning (RL) approach that learns an optimal bid strategy. Finally, numerical experiments are conducted to illustrate the superiority of the bid strategy learned by RL and to analyze the behavior of the bid strategy. A numerical analysis shows that the bid strategies learned by RL provide more rewards and lower costs than other benchmark strategies. In addition, a comparison of price-based and multi-attribute strategies reveals that the choice of appropriate strategies is situation-dependent. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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15 pages, 2502 KB

Open AccessArticle

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 - 1 May 2025

Cited by 2 | Viewed by 1889

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 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)

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26 pages, 5652 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1170

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 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)

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Review

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30 pages, 2663 KB

Open AccessReview

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 1549

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 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)

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