| Title | Machine Learning-Based Predictive Maintenance for Smart Manufacturing Systems |
| Research Area | Predictive Maintenance |
| Abstract | Modern manufacturing environments increasingly depend on automated machines, connected sen-sors, robotic systems, and continuously operating production equipment. Although these technolo-gies improve productivity, an unexpected equipment failure can interrupt production, increase maintenance expenditure, reduce product quality, and create safety concerns. Conventional mainte-nance strategies generally rely on either repairing equipment after failure or servicing equipment ac-cording to predetermined schedules. Such approaches do not always account for the actual operating condition of a machine. Predictive maintenance offers an alternative by using continuously collected operational data to estimate equipment condition and identify possible failures before they become critical. Machine learning has become an important technology for predictive maintenance because it can identify complex relationships among temperature, vibration, pressure, rotational speed, current, load, and other machine variables. Recent literature shows growing use of machine learning and deep learning for fault diagnosis, failure prediction, and remaining useful life estimation in industrial environments |
| Keywords | Internet of Things, Intrusion Detection, Artificial Intelligence, Machine Learning, Edge Computing, Anomaly Detection, Cybersecurity, IoT Networks, Lightweight Security |
| Paper Status | Published |
| Volume | 2 |
| Issue | 4 |
| Published On | 05/08/2026 |
| Published File |
IJSRTD_4888.pdf
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