Intelligent Fault Diagnosis and Prognosis in Electric Motors and Energy Storage Systems

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

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