DEVELOPMENT OF AN IoT-BASED INDUSTRIAL MONITORING SYSTEM FOR PREDICTIVE MAINTENANCE

Industrial operations increasingly demand high equipment reliability and operational efficiency to remain competitive. Traditional maintenance strategies, such as reactive or time-based preventive maintenance, often lead to unexpected equipment failures, unnecessary servicing, and increased operational costs. This study aims to develop an IoT-based industrial monitoring system for predictive maintenance, enabling real-time assessment of machine conditions and proactive maintenance decision-making. The system integrates IoT sensors to continuously monitor critical machine parameters, including temperature, vibration, pressure, and electrical usage, while a cloud-based dashboard displays live status, historical trends, and automated alerts. A developmental research design was used to come up with, build, and test the system. This was done using the design–develop–evaluate framework. Twelve respondents, including maintenance personnel, engineers, IT specialists, and subject matter experts, provided feedback through structured observation, expert evaluation forms, and system-generated sensor data. Results indicate that the IoT-enabled system accurately monitors machine conditions, detects anomalies, predicts potential failures, and delivers timely maintenance alerts. High scores in functionality, reliability, and usability demonstrate its effectiveness in reducing unplanned downtime, improving maintenance scheduling, and enhancing overall operational efficiency. The conversation shows that combining IoT technology with predictive analytics supports data-driven maintenance practices and meets Industry 4.0 standards. This makes it a scalable and useful solution for industrial facilities. The study concludes that IoT-based predictive maintenance can transform traditional maintenance approaches by minimizing operational disruptions, optimizing resource utilization, and extending equipment lifespan. Recommendations include system optimization, personnel training, and further research on long-term cost-effectiveness and integration with enterprise-level management platforms.