| Title | Predictive Maintenance of Industrial Equipment Using Machine Learning and IoT Sensor Data |
| Research Area | Predictive Maintenance |
| Abstract | Industrial organizations increasingly depend on connected machines, automated production lines, and sensor-based monitoring systems. Unexpected equipment failures can interrupt production, in-crease maintenance expenses, reduce product quality, and create safety risks. Traditional mainte-nance practices generally follow fixed schedules or respond to failures after they occur. Although preventive maintenance is more reliable than purely reactive maintenance, scheduled servicing may result in unnecessary component replacement because equipment condition can vary significantly during operation. The integration of Internet of Things (IoT) devices with machine learning provides an opportunity to develop predictive maintenance systems that estimate equipment health and iden-tify possible failures before they cause significant operational disruption. |
| Keywords | Predictive Maintenance, Internet of Things, Machine Learning, Industrial IoT, Equip-ment Failure, Sensor Analytics, Condition Monitoring, Anomaly Detection, Industry 4.0 |
| Paper Status | Published |
| Volume | 2 |
| Issue | 4 |
| Published On | 14/08/2026 |
| Published File |
IJSRTD_4890.pdf
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