Investigation into the Use of AI-Based Predictive Maintenance for University IT Infrastructure: A Case Study of Federal University, Birnin Kebbi (Birnin Kebbi LGA, Kebbi State)
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- NGN 5000
Background of the Study
In today's educational landscape, universities rely heavily on information technology (IT) infrastructure for managing academic resources, administrative functions, communication, and research. This increasing dependence on IT systems necessitates regular maintenance to ensure smooth operations. Traditional maintenance practices, such as scheduled or reactive repairs, often lead to extended downtimes, higher costs, and system inefficiencies (Ahmed & Tukur, 2024). Predictive maintenance, powered by artificial intelligence (AI), represents a promising solution to these challenges. By utilizing machine learning models to predict equipment failure before it occurs, AI-based predictive maintenance systems can help universities optimize the lifespan of IT infrastructure and reduce operational disruptions (Olumide et al., 2025).
Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State, provides an excellent case study for implementing AI-based predictive maintenance in the higher education sector. The university, like many others, faces challenges in maintaining its IT infrastructure, including computer labs, servers, networking equipment, and other critical systems. Leveraging AI to predict and address IT failures before they occur could significantly reduce maintenance costs, minimize downtime, and improve the overall efficiency of university operations (Eze & Musa, 2023). This research aims to evaluate the effectiveness of AI-based predictive maintenance in optimizing the performance of IT infrastructure at Federal University, Birnin Kebbi.
Statement of the Problem
Federal University, Birnin Kebbi, faces challenges related to the high costs and inefficiencies of its traditional IT maintenance approach. Frequent equipment failures and unexpected downtimes disrupt both academic and administrative activities, leading to productivity losses. Current maintenance practices are often reactive, only addressing problems after they occur. This not only increases the time spent on repairs but also reduces the overall reliability of the university's IT infrastructure. The introduction of AI-based predictive maintenance systems could offer a solution by forecasting equipment failures in advance, thus enabling timely intervention and reducing the risk of system outages.
Objectives of the Study
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To design and implement an AI-based predictive maintenance system for IT infrastructure at Federal University, Birnin Kebbi.
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To assess the effectiveness of the AI-based predictive maintenance system in reducing downtime and maintenance costs at the university.
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To evaluate the impact of AI-based predictive maintenance on the overall performance and reliability of IT infrastructure at the university.
Research Questions
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How effective is the AI-based predictive maintenance system in reducing downtime and maintenance costs for IT infrastructure at Federal University, Birnin Kebbi?
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What impact does the AI-based predictive maintenance system have on the reliability and performance of IT infrastructure at the university?
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How do university IT staff perceive the use of AI-based predictive maintenance in managing the university's IT infrastructure?
Significance of the Study
This study will contribute to the growing body of knowledge on the application of AI in managing IT infrastructure in educational institutions. By providing a framework for predictive maintenance, it could help Federal University, Birnin Kebbi, and other universities optimize their IT operations, reduce costs, and ensure greater system reliability, ultimately enhancing the overall academic and administrative experience.
Scope and Limitations of the Study
This study will focus on the implementation of AI-based predictive maintenance systems for IT infrastructure at Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State. The scope will include IT equipment such as servers, computers, and network systems, and data will be collected from university IT staff and performance records.
Definitions of Terms
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AI-Based Predictive Maintenance: The use of artificial intelligence algorithms to predict and prevent equipment failure by analyzing historical data and identifying patterns that indicate potential issues.
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IT Infrastructure: The physical and virtual components that support the IT systems and services of an organization, including servers, networks, and devices.
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Downtime: The period during which a system or equipment is unavailable due to maintenance or failure, resulting in a loss of productivity.
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