REAL-TIME CONDITION SURVEILLANCE AND INCIPIENT FAULT IDENTIFICATION IN THREE-PHASE INDUCTION MOTORS
REAL-TIME CONDITION SURVEILLANCE AND INCIPIENT FAULT IDENTIFICATION IN THREE-PHASE INDUCTION MOTORS
Nikita S. More1, Ashwini A. Pawar2, Aditya S. Ujagare3, Armaan S. Zari4, Shrikant D. Mangate5
1,2,3,4Students, 5Asst. Prof.
Department of Electrical Engineering SVPM’s COE, Malegaon (BK), Baramati, India
Savitribai Phule Pune University nikitamore0311@gmail.com1, ap215713@gmail.com2, adityaujagare02@gmail.com3,
armaan.zari.2805@gmail.com4, sdmangate@engg.svpm.org.in5
Abstract—Three-phase induction motors serve as the electromechanical backbone of contemporary industrial plants, valued for their rugged construction and favorable cost-efficiency ratio. Sustained operation in demanding environments, however, exposes them to compounding electrical, thermal, and mechanical degradation mechanisms that can escalate from incipient anomalies into catastrophic failures, incurring costly unplanned outages and safety exposure. This work presents an IoT-enabled continuous parameter-surveillance platform centered on an ESP32 microcontroller. The system acquires winding temperature, three phase current and voltage, and mechanical vibration data from dedicated transducers, benchmarks each measurement against configurable alarm thresholds, and relays timestamped telemetry to a cloud-hosted dashboard via Wi-Fi. Graduated alerts are issued when parameters drift toward critical limits, enabling corrective intervention before irreversible damage materializes. A bench prototype was assembled and exercised under no-load conditions, yielding measurements that validated the sensing chain and threshold logic. This paper elaborates on system architecture, detection strategy, firmware design, hardware realization, and experimental outcomes.
Index Terms—Three-phase induction motor, condition surveil- lance, incipient fault identification, ESP32, IoT dashboard, predictive maintenance, embedded monitoring