Advanced Technologies for Motor Condition Monitoring
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 320
Special Issue Editor
Interests: design of permanent magnent synchronous motor; high-performance motor control algorithm; embedded encoder; parameter identification; sensorless control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue invites advanced research on motor condition monitoring, focusing on the integration of artificial intelligence (AI), parameter identification, and novel sensor technologies. With the rapid evolution of Industry 4.0 and smart manufacturing, ensuring the reliability, efficiency, and safety of electric motors has become critical across industries such as industrial automation, robotics, electric vehicles, and renewable energy systems. This Special Issue aims to explore innovative methodologies that bridge the gap between theoretical advancements and practical applications, addressing challenges such as real-time data processing, fault prediction, and adaptive control.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- AI-driven anomaly detection and fault diagnosis: Leveraging machine learning (e.g., deep neural networks and reinforcement learning) and data fusion techniques to identify motor faults, such as bearing wear, rotor imbalance, and insulation degradation.
- Dynamic parameter identification: Advanced algorithms for the real-time estimation of motor parameters (e.g., resistance, inductance, and inertia) under varying operational conditions. Examples include adaptive estimation frameworks and hybrid models combining physics-based and data-driven approaches.
- Smart sensor technologies: Development of embedded sensors (e.g., AI-integrated current/voltage sensors and MEMS-based vibration sensors) and wireless sensor networks for high-precision, low-latency data acquisition.
- Digital twin applications: Virtual modeling of motor systems for predictive maintenance, integrating real-time sensor data with physics-based simulations to optimize performance and longevity.
- Multimodal data fusion and collaborative analytics: AI-driven integration of heterogeneous data (current, vibration, and temperature) for real-time monitoring, fault prediction, and enhanced reliability in motor systems.
I/we look forward to receiving your contributions.
Prof. Dr. Wei Hua
Guest Editor
Manuscript Submission Information
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Keywords
- AI-driven monitoring
- parameter identification
- smart sensors
- fault diagnosis
- digital twins
- predictive maintenance
- deep learning
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