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Technologies
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Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control
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Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control

A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 1 July 2026 | Viewed by 2617

Special Issue Editor

Dr. David Granados-Lieberman

E-Mail Website
Guest Editor
ENAP-Research Group, Departamento de Ingeniería Electromecánica, Tecnológico Nacional de México, ITS Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, El Copal, Irapuato 36821, Guanajuato, Mexico
Interests: signal processing; smart fault diagnosis; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing complexity and interconnectivity of modern engineering systems—ranging from autonomous vehicles and smart grids to renewable energy systems, industrial automation, and cyber–physical infrastructures—has amplified the need for intelligent, scalable, and efficient fault diagnosis and fault-tolerant control solutions. Traditional model-based techniques, while foundational, often struggle to cope with the challenges posed by nonlinear dynamics, high-dimensional data, and large-scale system integration.

This Special Issue aims to showcase original research and comprehensive reviews on cutting-edge Artificial Intelligence (AI) methodologies, including machine learning (ML), deep learning (DL), hybrid, and multimodal approaches for smart fault detection, isolation, diagnosis (FDI), and fault-tolerant control (FTC).

We welcome contributions that address both theoretical developments and real-world applications, highlighting how AI-driven models can enhance reliability, adaptivity, and resilience in complex systems.

Topics of interest include (but are not limited to) the following:

  • Supervised and unsupervised learning for fault detection and diagnosis;
  • Reinforcement learning for adaptive and fault-tolerant control;
  • Federated, distributed, and online learning approaches;
  • Digital twin-based fault diagnosis and control;
  • Interpretable and explainable AI for safety-critical systems;
  • Hybrid and multi-model learning strategies;
  • Multimodal sensor fusion for FDI;
  • AI-enhanced condition monitoring and anomaly detection.
Application areas include the following:
  • Autonomous and intelligent transportation systems;
  • Smart grids and renewable energy systems;
  • Industrial automation, manufacturing, and robotics;
  • Cyber–physical systems and intelligent infrastructure;
  • Water distribution and environmental monitoring;
  • Air quality monitoring and climate control systems.

We invite researchers and practitioners from both academia and industry to submit innovative methodologies, case studies, and benchmarking results that advance the state of the art in smart fault diagnosis and resilient control systems.

We look forward to receiving your valuable contributions to this Special Issue.

Dr. David Granados-Lieberman
Guest Editor

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. Technologies 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

  • fault diagnosis
  • fault-tolerant control
  • machine learning
  • deep learning
  • hybrid intelligence
  • fault detection and isolation (FDI)
  • interpretable AI
  • digital twin
  • cyber-physical systems
  • autonomous systems
  • intelligent systems
  • smart grids
  • renewable energy systems
  • industrial automation
  • condition monitoring
  • anomaly detection
  • supervised learning
  • unsupervised learning
  • federated learning
  • reinforcement learning

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Published Papers (4 papers)

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21 pages, 3724 KB  
Article
Fault Diagnosis for IP-Based Networks Using Incremental Learning Algorithms and Data Stream Methods
by Angela María Vargas-Arcila, Angela Rodríguez-Vivas, Juan Carlos Corrales, Araceli Sanchis and Álvaro Rendón Gallón
Technologies 2026, 14(2), 132; https://doi.org/10.3390/technologies14020132 - 19 Feb 2026
Viewed by 307
Abstract
Network fault diagnosis has evolved in response to the needs of modern networks, transitioning from traditional methods, such as passive and active monitoring, to advanced learning techniques. While conventional methods often introduce invasive traffic and control overhead, newer approaches face challenges such as [...] Read more.
Network fault diagnosis has evolved in response to the needs of modern networks, transitioning from traditional methods, such as passive and active monitoring, to advanced learning techniques. While conventional methods often introduce invasive traffic and control overhead, newer approaches face challenges such as increased internal processes and the need for extensive knowledge of network behavior. Learning-based methods offer an advantage by not requiring a complete network model, allowing the use of statistical and Machine Learning techniques to process historical data. However, existing learning methods face limitations, such as the need for extensive data samples and extended retraining periods, which can leave systems vulnerable to failures, particularly in dynamic environments. This work addresses these issues by proposing an incremental learning approach for continuous fault diagnosis in IP-based networks. The approach utilizes online learning to process symptoms in real-time, adapting to network changes while managing data imbalance through drift detection and rebalancing strategies, such as ADWIN and SMOTE. We evaluated the performance of this method using 25 incremental algorithms on the SOFI dataset. The results, assessed using metrics such as recall, G-mean, kappa, and MCC, demonstrated high performance over time, indicating the potential for resilient, adaptive fault detection processes in dynamic network environments. Additionally, a non-invasive process can be ensured through peripheral observation of failure symptoms, provided that data collection does not increase network traffic, overhead control, or internal network processes. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control)
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41 pages, 4547 KB  
Article
Online Fault Detection, Classification and Localization in PV Arrays Using Feedforward Neural Networks and Residual-Based Modeling
by Kareem Adel Mohamed, Nahla E. Zakzouk, Mostafa Abdelgeliel and Karim H. Youssef
Technologies 2026, 14(2), 130; https://doi.org/10.3390/technologies14020130 - 18 Feb 2026
Viewed by 346
Abstract
Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high [...] Read more.
Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control)
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25 pages, 761 KB  
Article
Deep Reinforcement Learning-Based Voltage Regulation Using Electric Springs in Active Distribution Networks
by Jesus Ignacio Lara-Perez, Gerardo Trejo-Caballero, Guillermo Tapia-Tinoco, Luis Enrique Raya-González and Arturo Garcia-Perez
Technologies 2026, 14(2), 87; https://doi.org/10.3390/technologies14020087 - 1 Feb 2026
Viewed by 313
Abstract
The increasing penetration of distributed generation in active distribution networks (ADNs) introduces significant voltage regulation challenges due to the intermittent nature of renewable energy sources. Electric springs (ESs) have emerged as a cost-effective alternative to conventional FACTS devices for voltage regulation, requiring minimal [...] Read more.
The increasing penetration of distributed generation in active distribution networks (ADNs) introduces significant voltage regulation challenges due to the intermittent nature of renewable energy sources. Electric springs (ESs) have emerged as a cost-effective alternative to conventional FACTS devices for voltage regulation, requiring minimal energy storage while providing fast, flexible reactive power compensation. This paper proposes a deep reinforcement learning (DRL)-based approach for voltage regulation in balanced active distribution networks with distributed generation. Electric springs are deployed at selected buses in series with noncritical loads to provide flexible voltage support. The main contributions of this work are: (1) a novel region-based penalized reward function that effectively guides the DRL agent to minimize voltage deviations; (2) a coordinated control strategy for multiple ESs using the Deep Deterministic Policy Gradient (DDPG) algorithm, representing the first application of DRL to ES-based voltage regulation; (3) a systematic hyperparameter tuning methodology that significantly improves controller performance; and (4) comprehensive validation demonstrating an approximately 40% reduction in mean voltage deviation relative to the no-control baseline. Three well-known continuous-control DRL algorithms, Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and DDPG, are first evaluated using the default hyperparameter configurations provided by MATLAB R2022b.Based on this baseline comparison, a dedicated hyperparameter-tuning procedure is then applied to DDPG to improve the robustness and performance of the resulting controller. The proposed approach is evaluated through simulation studies on the IEEE 33-bus and IEEE 69-bus test systems with time-varying load profiles and fluctuating renewable generation scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control)
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50 pages, 3579 KB  
Article
Safety-Aware Multi-Agent Deep Reinforcement Learning for Adaptive Fault-Tolerant Control in Sensor-Lean Industrial Systems: Validation in Beverage CIP
by Apolinar González-Potes, Ramón A. Félix-Cuadras, Luis J. Mena, Vanessa G. Félix, Rafael Martínez-Peláez, Rodolfo Ostos, Pablo Velarde-Alvarado and Alberto Ochoa-Brust
Technologies 2026, 14(1), 44; https://doi.org/10.3390/technologies14010044 - 7 Jan 2026
Viewed by 758
Abstract
Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with [...] Read more.
Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling (R2=0.89), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control)
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