Fault Detection in Induction Motors

Dear Colleagues,

Induction motors (IMs) are critical electromechanical devices in modern society, found across a vast range of industrial, commercial, residential, energy, and other sectors. Despite their robustness, they are prone to failures, making their reliable operation a critical concern. Recent advances in sensor technology, industrial information systems, artificial intelligence techniques, and predictive algorithms have enabled a new generation of approaches for fault detection, diagnostics, and prognostics. However, significant challenges remain, particularly in achieving timely, automated, and continuous diagnostics under diverse and variable operational conditions.

To address these challenges, this Special Issue welcomes original contributions focused on developing robust models through advanced signal processing, artificial intelligence, and multi-domain data fusion. Topics include, but are not limited to, the following:

• Signal Processing and Analysis: Advanced techniques for time-frequency decomposition and signal analysis.
• Pattern Recognition and Classification: Novel algorithms and methods for fault detection and diagnosis.
• Artificial Intelligence and Deep Learning: Application of AI, neural networks, and deep learning models to condition monitoring, diagnosis, and prognosis using diverse signals (e.g., vibration, current, acoustic emissions, magnetic flux).
• Multisensor Data Fusion: Strategies for integrating information from multiple sensors and physical domains.
• Industrial Applications and Case Studies: Real-world implementations, validation studies, and practical challenges in industrial settings.

Dr. Israel Zamudio-Ramirez
Prof. Dr. Jose Alfonso Antonino-Daviu
Guest Editors

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