The role of AI-based predictive maintenance in library infrastructure in Taraba State College of Education Library, Zing, Taraba State
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Background of the study
Predictive maintenance uses AI and IoT sensor data to forecast equipment failures—such as HVAC systems, lighting, and automated doors—allowing timely interventions before breakdowns occur (Lee et al., 2014). In library environments, uninterrupted infrastructure services are critical for user comfort and resource preservation (Perez, 2024). Taraba State College of Education Library in Zing implemented AI‑powered predictive maintenance for its climate control and security systems, using sensor networks and machine learning models to detect anomalies and schedule maintenance (Eze, 2025). While the system has reduced unscheduled downtime, systematic evaluation of prediction accuracy, maintenance cost savings, and user satisfaction with facility reliability is needed. Challenges include sensor calibration, integration with maintenance workflows, and staff training on interpreting predictive alerts.
Statement of the problem
Despite predictive maintenance deployment, library staff at Zing report occasional false alarms and missed detections, leading to maintenance inefficiencies and continued reliance on reactive repairs. Without empirical assessment of model performance and workflow integration, the library cannot optimize predictive maintenance strategies or justify further investment.
Objectives of the study
To evaluate the accuracy and lead time of AI-based failure predictions for critical library systems.
To measure maintenance cost savings and reduction in unscheduled downtime.
To identify sensor network and workflow adjustments needed for effective predictive maintenance.
Research questions
What is the precision and recall of predictive maintenance alerts for HVAC and security systems?
How have maintenance costs and downtime incidents changed post‑implementation?
What technical and organizational factors influence predictive maintenance success?
Significance of the study
The study will guide facilities managers and library administrators in refining sensor deployments, AI model configurations, and maintenance protocols to ensure reliable infrastructure services, improve user comfort, and optimize operational budgets at Zing Library.
Scope and limitations of the study
This investigation focuses on AI-based predictive maintenance for HVAC and security infrastructure at Taraba State College of Education Library. It excludes IT system maintenance and non‑sensorized equipment. Limitations include sensor data quality variability and environmental factors affecting system performance.
Definitions of terms
Predictive maintenance: AI-driven approach forecasting equipment failures based on sensor data and analytics.
Precision: Proportion of predictive alerts that correctly forecast failures.
Recall: Proportion of actual failures that are successfully predicted by the system.
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