Diagnosis of Sensor Failure in Induction Motor Drives

Dear Colleagues,

Accurate sensing of voltage, current, speed/position, and temperature is critical for the safe and efficient operation of induction motor drives (IMDs). Sensors can degrade torque control, reduce efficiency, and trigger costly downtime. This Special Issue seeks contributions on the diagnosis, modeling, mitigation, and validation of sensor failures in IMDs, encouraging works that integrate analytical calculation and equivalent-circuit/state-space models with signal-, observer-, and data-driven methods, and that report reproducible experiments. We welcome signal-based methods (e.g., MCSA/FFT, time–frequency analysis), observer-based FDI/FTC, and data-driven/ML techniques benchmarked against physics-grounded baselines, as well as studies employing hardware-/power-hardware-in-the-loop with realistic fault injection and reproducibility assets (datasets, parameter extraction).

Research topics of interest include, but are not limited to, the following:

• Sensor fault modeling, detection, isolation, and tolerant control in IMDs (speed/position, current/voltage, temperature).
• Analytical calculation and equivalent-circuit/state-space formulations for fault diagnosis and performance prediction.
• Signal-based diagnostics (MCSA/FFT, time–frequency, high-frequency signal injection, negative-sequence/space-vector indicators).
• Observer/estimator methods (Luenberger, EKF/UKF, SMO, MRAS) for sensor fault detection and reconstruction.
• Data-driven/ML/DL diagnostics; domain shift, nonstationarity, and class-imbalance handling; hybrid physics–ML approaches.
• Hardware-/power-hardware-in-the-loop (HIL/PHIL) validation, scalable fault injection, and benchmark datasets.
• Sizing, derating, and thermal co-design under sensor faults; impact on efficiency, torque ripple, and power quality.
• Application studies (industrial drives, EV traction, renewables, autonomous systems) with reproducibility and open artifacts.

Dr. Solmaz Kahourzade
Guest Editor

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• induction motor drives
• sensor fault diagnosis
• equivalent-circuit and analytical modeling
• observer-based FDI/FTC
• signal-based methods (MCSA/FFT, time–frequency)
• machine learning for diagnostics
• hardware-/power-hardware-in-the-loop
• sizing and performance validation