AI powered health monitoring for electrical machines using machine learning

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dc.contributor.authorRabia Yasmeen
dc.contributor.authorAbdul Rehman Atiq
dc.contributor.authorMuhammad Anas
dc.date.accessioned2025-12-22T10:18:34Z
dc.date.available2025-12-22T10:18:34Z
dc.date.issued2025
dc.description.abstractThis project develops and demonstrates a low-cost AI powered health monitoring system for a single-phase capacitor-start induction motor using an Aurdino UNO microcontroller and sensors (ACS712 for current, ZMPT101B for voltage, DS18B20 for winding temperature and SW-420 for vibration pulses). The firmware acquires synchronized measurements in short sliding windows, computes electrical, thermal and vibration features, and applies a simple, interpretable random Foresto classify the motor state as healthy or faulty in real time. Sensor data and health status are streamed to a Google Sheet for historical logging and displayed on a Blynk mobile dashboard for remote visibility and alerts. Controlled tests with induced overload, restricted cooling and imbalance scenarios show that the system detects abnormal conditions within a few seconds, maintains stable readings during normal operation. The results confirm that an explainable edge AI approach can provide timely, reliable condition monitoring for motors without expensive industrial hardware, laying a practical foundation for future multi-class diagnostics and predictive maintenance enhancements.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/17658
dc.language.isoen
dc.publisherUMT Lahore
dc.titleAI powered health monitoring for electrical machines using machine learning
dc.typeThesis
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