Artificial Intelligence for Smart Fault Diagnosis and Fault Tolerant Control

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.

• 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. Majdi Mansouri
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 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. 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 1600 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.

• 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