MACHINE LEARNING-BASED PRECTIVE MAINTANANCE CAMPUS EQUIPMENT

Machine learning–based predictive maintenance is transforming how campus equipment is monitored, managed, and maintained. Traditional maintenance approaches, such as reactive and scheduled maintenance, often lead to unexpected equipment failures, increased downtime, and higher operational costs. In contrast, predictive maintenance leverages data-driven techniques to anticipate failures before they occur, enabling timely interventions and more efficient resource allocation.
This study explores the application of machine learning algorithms to improve the reliability and lifespan of campus equipment, including HVAC systems, laboratory instruments, power infrastructure, and IT hardware. By collecting real-time and historical data from sensors embedded in equipment, key performance indicators such as temperature, vibration, energy consumption, and usage patterns can be analyzed. Machine learning models, including supervised learning methods like regression and classification, as well as unsupervised techniques such as anomaly detection, are employed to identify patterns indicative of potential faults supervised learning techniques help.
The proposed system integrates data acquisition, preprocessing, feature extraction, model training, and deployment into a unified framework. Experimental results demonstrate that machine learning models can accurately predict equipment failures with high precision, significantly reducing unplanned downtime and maintenance costs. Furthermore, the system supports decision-making by providing actionable insights and maintenance schedules tailored to specific equipment conditions. Reactive maintenance increases downtime and operational costs
The findings highlight the potential of predictive maintenance to enhance operational efficiency and sustainability within campus environments. By minimizing energy waste and extending equipment life, and improving service reliability, institutions can achieve both economic and environmental benefits. Future work will focus on integrating advanced techniques such as deep learning and Internet of Things (IoT) platforms to further improve predictive accuracy and scalability. Overall, machine learning–based predictive maintenance represents a proactive and intelligent approach to modern campus facility management.