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Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent...
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Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8511

Special Issue Editors

Dr. Hugo Landaluce

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48940 Bilbao, Spain
Interests: communication protocols; edge-AI; PLCs; RFID
Special Issues, Collections and Topics in MDPI journals
Dr. Ignacio Angulo Martínez

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Interests: embedded systems; remote laboratories; edge-AI
Dr. Ander Garcia

E-Mail Website
Guest Editor
Data Intelligence for Industry, Vicomtech, Paseo Mikeletegi 57, 20009 San Sebastián, Spain
Interests: cyber physical systems; embedded systems; Industry 4.0

Special Issue Information

Dear Colleagues,

The primary aim of this Special Issue is to explore the transformative role of Artificial Intelligence (AI) in solving practical industrial problems across the full data lifecycle—from acquisition to actionable insights. While AI has traditionally been associated with prediction and automation, its integration with sensor systems, edge devices, and industrial networks opens new avenues for intelligent decision-making, real-time optimization, and adaptive control in complex industrial environments.

The scope of this Special Issue encompasses the application of AI methods in diverse industrial domains such as manufacturing, energy, logistics, transportation, and smart infrastructure. Topics include AI-driven approaches for sensor data acquisition, fusion, real-time processing, anomaly detection, fault diagnosis, process optimization, and predictive maintenance. We aim to gather interdisciplinary research that not only showcases successful implementations but also addresses technical challenges such as data sparsity, model robustness, and the integration of AI into legacy systems.

This Special Issue seeks to supplement the existing literature by bridging the gap between the theory of AI and its deployment in industrial practice. While numerous studies have advanced AI methods in controlled or simulated settings, this Special Issue emphasizes applied research that demonstrates tangible improvements in industrial performance. In doing so, it contributes to the evolving discourse on Industry 4.0 and AI-driven digital transformation, offering both methodological insights and practical case studies.

Dr. Hugo Landaluce
Dr. Ignacio Angulo Martínez
Dr. Ander Garcia
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 250 words) can be sent to the Editorial Office for assessment.

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. AI 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 1800 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

  • artificial intelligence
  • industrial applications
  • sensor systems
  • predictive maintenance
  • smart manufacturing
  • intelligent decision-making
  • data acquisition and analysis

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Published Papers (5 papers)

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Research

55 pages, 2886 KB  
Article
Hybrid AI and LLM-Enabled Agent-Based Real-Time Decision Support Architecture for Industrial Batch Processes: A Clean-in-Place Case Study
by Apolinar González-Potes, Diego Martínez-Castro, Carlos M. Paredes, Alberto Ochoa-Brust, Luis J. Mena, Rafael Martínez-Peláez, Vanessa G. Félix and Ramón A. Félix-Cuadras
AI 2026, 7(2), 51; https://doi.org/10.3390/ai7020051 - 1 Feb 2026
Viewed by 1813
Abstract
A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and [...] Read more.
A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency Γs≥0.98), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making)
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22 pages, 10669 KB  
Article
Real-Time Optimal Parameter Recommendation for Injection Molding Machines Using AI with Limited Dataset
by Bipasha Roy, Silvia Krug and Tino Hutschenreuther
AI 2026, 7(2), 49; https://doi.org/10.3390/ai7020049 - 1 Feb 2026
Viewed by 1028
Abstract
This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated [...] Read more.
This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated framework, combining a genetic algorithm (GA) with a CatBoost-based surrogate model for multi-objective optimization of the injection molding machine parameters. The aim of the optimization is to minimize the cycle time and cycle energy while maintaining the product quality. Ten process parameters were optimized, which are machine-specific. An evolutionary optimization using the NSGA-II algorithm is used to generate the recommended parameter set. The proposed GA-surrogate hybrid approach produces the optimal set of parameters that reduced the cycle time by 4.5%, for this specific product, while maintaining product quality. Cycle energy was evaluated on an hourly basis; its variation across candidate solutions was limited, but it was retained as an optimization objective to support energy-based process optimization. A total of 95% of the generated solutions satisfied industrial quality constraints, demonstrating the robustness of the proposed optimization framework. While classical Design of Experiment (DOE) approaches require sequential physical trials, the proposed GA-surrogate framework achieves convergence in computational iterations, which significantly reduces machine usage for optimization. This approach demonstrates a practical way to automate data-driven process optimization in an injection mold machine for an industrial application, and it can be extended to other manufacturing systems that require adaptive control parameters. Full article
(This article belongs to the Special Issue Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making)
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14 pages, 992 KB  
Article
DVAD: A Dynamic Visual Adaptation Framework for Multi-Class Anomaly Detection
by Han Gao, Huiyuan Luo, Fei Shen and Zhengtao Zhang
AI 2025, 6(11), 289; https://doi.org/10.3390/ai6110289 - 8 Nov 2025
Cited by 1 | Viewed by 1714
Abstract
Despite the superior performance of existing anomaly detection methods, they are often limited to single-class detection tasks, requiring separate models for each class. This constraint hinders their detection performance and deployment efficiency when applied to real-world multi-class data. In this paper, we propose [...] Read more.
Despite the superior performance of existing anomaly detection methods, they are often limited to single-class detection tasks, requiring separate models for each class. This constraint hinders their detection performance and deployment efficiency when applied to real-world multi-class data. In this paper, we propose a dynamic visual adaptation framework for multi-class anomaly detection, enabling the dynamic and adaptive capture of features based on multi-class data, thereby enhancing detection performance. Specifically, our method introduces a network plug-in, the Hyper AD Plug-in, which dynamically adjusts model parameters according to the input data to extract dynamic features. By leveraging the collaboration between the Mamba block, the CNN block, and the proposed Hyper AD Plug-in, we extract global, local, and dynamic features simultaneously. Furthermore, we incorporate the Mixture-of-Experts (MoE) module, which achieves a dynamic balance across different features through its dynamic routing mechanism and multi-expert collaboration. As a result, the proposed method achieves leading accuracy on the MVTec AD and VisA datasets, with image-level mAU-ROC scores of 98.8% and 95.1%, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making)
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42 pages, 4717 KB  
Article
Intelligent Advanced Control System for Isotopic Separation: An Adaptive Strategy for Variable Fractional-Order Processes Using AI
by Roxana Motorga, Vlad Mureșan, Mihaela-Ligia Ungureșan, Mihail Abrudean, Honoriu Vǎlean and Valentin Sita
AI 2025, 6(10), 246; https://doi.org/10.3390/ai6100246 - 1 Oct 2025
Viewed by 1001
Abstract
This paper provides the modeling, implementation, and simulation of fractional-order processes associated with the production of the enriched 13C isotope due to chemical exchange processes between carbamate and CO2. To demonstrate and simulate the process most effectively, an execution of [...] Read more.
This paper provides the modeling, implementation, and simulation of fractional-order processes associated with the production of the enriched 13C isotope due to chemical exchange processes between carbamate and CO2. To demonstrate and simulate the process most effectively, an execution of a new approximating solution of fractional-order systems is required, which has become possible due to the utilization of advanced AI methods. As the separation process exhibits extremely strong nonlinearity and fractional-order-based performance, it was similarly necessary to utilize the fractional-order system theory to mathematically model the operation, which consists of the comparison of its output with an integrator function. The learning of the dynamic structure’s parameters of the derived fractional-order model is performed by neural networks, which are AI-based domain solutions. Thanks to the approximations executed, the concentration dynamics of the enriched 13C isotope can be simulated and predicted with a high level of precision. The solutions’ effectiveness is corroborated by the model’s response comparison with the reaction of the actual process. The current implementation uses neural networks trained specifically for this purpose. Furthermore, since the isotopic separation processes are long-settling-time processes, this paper proposes some control strategies that are developed for the 13C isotopic separation process, in order to improve the system performances and to avoid the loss of enriched product. The adaptive controllers were tuned by imposing them to follow the output of a first-order-type transfer function, using a PI or a PID controller. Finally, the paper confirms that AI solutions can successfully support the system throughout a range of responses, which paves the way for an efficient design of the automatic control for the 13C isotope concentration. Such systems can similarly be implemented in other industrial processes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making)
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22 pages, 7990 KB  
Article
Detection of Cracks in Low-Power Wind Turbines Using Vibration Signal Analysis with Empirical Mode Decomposition and Convolutional Neural Networks
by Angel H. Rangel-Rodriguez, Jose M. Machorro-Lopez, David Granados-Lieberman, J. Jesus de Santiago-Perez, Juan P. Amezquita-Sanchez and Martin Valtierra-Rodriguez
AI 2025, 6(8), 179; https://doi.org/10.3390/ai6080179 - 6 Aug 2025
Cited by 1 | Viewed by 1883
Abstract
Condition monitoring and fault detection in wind turbines are essential for reducing repair and maintenance costs. Early detection of faults enables timely interventions before the damage worsens. However, existing methods often rely on costly scheduled inspections or lack the ability to effectively detect [...] Read more.
Condition monitoring and fault detection in wind turbines are essential for reducing repair and maintenance costs. Early detection of faults enables timely interventions before the damage worsens. However, existing methods often rely on costly scheduled inspections or lack the ability to effectively detect early stage damage, particularly under different operational speeds. This article presents a methodology based on convolutional neural networks (CNNs) and empirical mode decomposition (EMD) of vibration signals for the detection of blade crack damage. The proposed approach involves acquiring vibration signals under four conditions: healthy, light, intermediate, and severe damage. EMD is then applied to extract time–frequency representations of the signals, which are subsequently converted into images. These images are analyzed by a CNN to classify the condition of the wind turbine blades. To enhance the final CNN architecture, various image sizes and configuration parameters are evaluated to balance computational load and classification accuracy. The results demonstrate that combining vibration signal images, generated using the EMD method, with CNN models enables accurate classification of blade conditions, achieving 99.5% accuracy while maintaining a favorable trade-off between performance and complexity. Full article
(This article belongs to the Special Issue Artificial Intelligence in Industrial Systems: From Data Acquisition to Intelligent Decision-Making)
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