Modelling, Condition Monitoring and Design of Electrical Machines

Dear Colleagues,

Electrical machines' condition monitoring and predictive maintenance have evolved significantly, driven by advancements in mathematical modeling, signal processing, and artificial intelligence (AI). These technologies enable early fault detection, reduce downtime, and enhance reliability and efficiency. Mathematical modeling is at the core of modern condition monitoring, which provides precise representations and simulations of machine behavior. These models are the foundation for diagnostic and prognostic techniques that offer insights into electrical machines' current state and future performance.

One of the most transformative developments in this area is the digital twin—a virtual replica of physical assets that functions in real-time. Integrating digital twin technology with signal processing makes it possible to continuously monitor the operational health of electrical machines. This integration allows for real-time parameter estimation and fault diagnosis, significantly improving predictive maintenance strategies. Adopting the Internet of Things (IoT) further enhances this process by enabling seamless data acquisition, transmission, and analysis. IoT networks facilitate continuous monitoring and deeper insights into machine dynamics by collecting extensive data from sensors embedded in electrical machines.

In condition monitoring, inverse problem theory is critical in parameter estimation. Electrical machine parameters, such as inductances, resistances, and capacitances, can be inferred from measurable quantities like current and voltage. Accurately solving these inverse problems is essential for developing reliable diagnostic algorithms. Advanced signal processing, combined with AI, offers effective tools for addressing these challenges. AI-driven models, such as machine learning and deep learning algorithms, can identify complex patterns in data, improving the accuracy of fault detection and prognosis.

Integrating AI, digital twins, and mathematical modeling provides a comprehensive framework for predictive maintenance. These technologies enable a shift from reactive to proactive maintenance, allowing issues to be identified and resolved before they escalate into major failures. This proactive approach is particularly valuable in critical applications where unplanned machine downtime can result in significant economic and operational losses.

This Special Issue focuses on the latest advancements in modeling, condition monitoring, fault-tolerant control, and design of electrical machines, emphasizing the interdisciplinary integration needed to enhance predictive maintenance. We invite contributions that explore novel mathematical models, AI-based diagnostic methods, and the practical application of digital twins and IoT in electrical machines. By converging diverse perspectives from these fields, we aim to spark innovative solutions for developing the next generation of intelligent and resilient electrical machines.

Dr. Bilal Asad
Dr. Toomas Vaimann
Guest Editors

Manuscript Submission Information

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• condition monitoring
• electrical machines
• mathematical modeling
• predictive maintenance
• signal processing
• digital twin
• Internet of Things
• parameters estimation
• inverse problem theory
• artificial intelligence