Intelligent Fault Diagnosis and Control Optimization for Electric Machines

Dear Colleagues,

This Special Issue, titled “Intelligent Fault Diagnosis and Control Optimization for Electric Machines”, is dedicated to presenting cutting-edge research and comprehensive reviews on the integration of artificial intelligence and advanced control systems. Contributions will focus on novel applications (such as electric vehicles, more-electric aircraft, and wind turbines, among others), emerging paradigms (including federated learning, explainable artificial intelligence, and digital twins), innovative architectures (for example, deep neural networks, hybrid models, and edge–cloud systems), and advanced methods (such as transfer learning, meta-learning, and model predictive control, to name a few). This issue’s primary goal is to explore data-driven solutions that move beyond traditional methods, enabling unprecedented levels of autonomy, reliability, and energy efficiency in modern industrial and commercial applications.

Suggested topics include those outlined below.

Intelligent fault diagnosis and prognostics:

• Next-generation fault diagnosis using deep learning models like transformers and graph neural networks.
• Advanced signal processing and fusion techniques for multi-sensor data (e.g., current, vibration, thermal, acoustic).
• Frameworks for robust prognostics and remaining useful life prediction under uncertainty.
• Methodologies for scenarios with limited labeled data, including self-supervised and few-shot learning.

Artificial intelligence-enhanced and adaptive control strategies:

• Real-time optimization of torque, speed, and efficiency using artificial intelligence-based controllers.
• Reinforcement learning and adaptive control for self-optimizing and fault-tolerant systems.
• Integration of fault diagnosis information with model predictive control for resilient operation.
• Co-design of control and diagnostic systems for enhanced performance and health management.

Synergistic and system-level approaches:

• Development and validation of dynamic digital twins for simulation, prediction, and health management.
• Explainable and trustworthy AI frameworks for transparent decision-making in safety-critical systems.
• Edge computing and embedded systems for real-time condition monitoring and control.

Comprehensive case studies and experimental validations in industrial, automotive, and aerospace settings.

Dr. Tarek Berghout
Dr. Athanasios Karlis
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. Machines 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.

• intelligent fault diagnosis
• predictive maintenance
• electric machines and drives
• artificial intelligence
• control optimization
• digital twin
• prognostics and health management
• signal processing
• deep learning
• condition monitoring