AI-Powered Predictive Maintenance System for Industrial Equipment: Reducing Downtime and Costs Through Sensor-B

Abstract

Unplanned equipment failures in industrial plants cause production interruptions, safety incidents, and costly emergency repairs. Traditional time-based preventive maintenance either under-services or over-services equipment, leading to inefficiencies and avoidable downtime. At the same time, modern machinery generates large amounts of sensor data (vibration, temperature, current, pressure) that are rarely exploited systematically. This paper presents an AI-Powered Predictive Maintenance System that uses multi-sensor data and machine learning to forecast failures before they occur, enabling condition-based interventions. The framework integrates edge sensing, feature extraction, supervised learning for Remaining Useful Life (RUL) prediction, and a decision layer that triggers maintenance actions based on risk thresholds. Validation on three classes of rotating machinery (pumps, motors, gearboxes) across two manufacturing plants shows that the system reduces unplanned downtime by 46.3%, maintenance costs by 29.7%, and maintenance-induced production losses by 24.1% compared to traditional preventive maintenance. Critical innovation: the framework maintains high prediction performance even in low-label settings by combining unsupervised anomaly detection with transfer learning from similar equipment. The system achieves an average F1-score of 0.92 in early-failure detection (at least 48 hours before failure) and reduces false alarms by 38% relative to simpler threshold-based monitoring. These results demonstrate that data-driven predictive maintenance can be feasibly deployed in brownfield industrial environments using existing sensors and low-cost edge devices, enabling scalable reliability improvement without major capital expenditure.