Advanced Artificial Intelligence Tools and Machine Learning Algorithms for Condition Monitoring of Electric Machines
Organized by: Prof. Jose Antonino-Daviu, Prof. Israel Zamudio, Prof. Hubert Razik, Prof. Athanasios Karlis
The application of advanced signal processing and Artificial Intelligence (AI) techniques to electric machines has proliferated in recent years. Many areas related to the design, modeling, control, diagnosis, or optimization of electric machines, among others, have increasingly adopted these recent technologies to enhance the results that years ago were not achievable with conventional methods. On the other hand, in the new era of the Internet of Things (IoT), machine learning, big data analysis and deep learning methods have proliferated. They leverage electric machine data to build intelligent algorithms that help to optimize, automate monitoring tasks, and apply transfer learning to diverse aspects related to these energy conversion devices, enhancing adaptability across different operating conditions and industrial applications. In this context, an intense research effort has been developed over recent years, merging AI and other IoT-related methods.
The aim of the special section is to provide a timely opportunity for researchers, practicing engineers and other stakeholders to share their latest discoveries related to the application of advanced artificial intelligence and machine learning techniques to electric machines. Submissions need to demonstrate strong original contributions to these areas:
- New signal processing algorithms applied to the control, design, monitoring and/or diagnostics of electric machines.
- Advanced AI techniques applied to electrical machines.
- Application of big data technologies to the control, design, monitoring and/or diagnostics of electric machines.
- Transfer learning techniques for electric machine condition monitoring.
- Deep learning-based sensor fusion for enhanced fault diagnosis in electric machines.
- IoT technologies applied to electric machines areas.
- Automatization of electric machines-related areas systems using modern technologies.
- Deep learning methods applied to electric machines.
- Design to digital twin-based systems adapted to electric machines applications.
- Blockchain and machine learning technologies applied to electric machines areas.
- Knowledge-driven intelligent optimization methods applied to electric machines areas.