Induction Motor

Induction motors are ubiquitous in industry, and ensuring their reliable operation is crucial. Current research heavily emphasizes developing advanced fault detection and predictive maintenance strategies, primarily leveraging machine learning algorithms like artificial neural networks and decision trees, often combined with signal processing techniques (e.g., FFT, wavelet transforms) to analyze motor current, voltage, vibration, temperature, and acoustic data. These methods aim to improve efficiency, reduce downtime, and enhance safety by enabling early identification of faults such as overheating, short circuits, and broken rotor bars. This work has significant implications for industrial automation and predictive maintenance, leading to more robust and reliable systems.