Intelligent Fault Diagnosis and Prognosis in Electric Motors and Energy Storage Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 311
Special Issue Editor
Interests: applied dynamics; artificial intelligence toward prognostics and health management (PHM); Robotic condition monitoring, diagnostic and prognostic systems; sensors and measurements; modeling and characterization of degradation phenomena; application of interests: complex energy systems including li-ion battery cells and wind energy conversion systems
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Special Issue Information
Dear Colleagues,
The increasing adoption of electric motors and energy storage systems across industrial, transportation, and renewable energy sectors demands advanced techniques for ensuring their reliability and operational efficiency. This Special Issue aims to explore recent advancements in intelligent fault diagnosis and prognosis methods applied to these critical components. Emphasizing the integration of artificial intelligence (AI), machine learning (ML), signal processing, and digital twin technologies, the issue seeks to present cutting-edge research and practical applications that enable early fault detection, accurate fault classification, remaining useful life (RUL) estimation, and predictive maintenance strategies.
Topics of interest include, but are not limited to, the following:
- AI/ML-based fault detection and diagnosis techniques;
- Condition monitoring and data-driven health assessment;
- Prognostic modeling for electric motors and batteries;
- Hybrid approaches combining physics-based and data-driven methods;
- Edge and cloud computing applications in real-time monitoring;
- Sensor fusion and advanced signal processing for fault analysis;
- Case studies and industrial applications.
This Special Issue aims to bring together researchers, engineers, and practitioners from academia and industry to foster cross-disciplinary collaboration and accelerate innovation in the intelligent maintenance of electric machines and energy storage systems. We look forward to receiving your contributions.
Dr. Ki-Yong Oh
Guest Editor
Manuscript Submission Information
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Keywords
- fault diagnosis
- prognostics
- electric motors
- energy storage systems
- battery management systems
- condition monitoring
- predictive maintenance
- remaining useful life (RUL)
- machine learning
- artificial intelligence
- digital twin
- data-driven methods
- smart monitoring systems
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