Improving The Rehabilitation And Maintenance Of Workshop Equipments Using Artificial Neural Network Based System. A Case Study Of Caritas University Enugu

Abstract

The efficient operation of workshop equipment is crucial for academic institutions, particularly in technical and vocational education. However, frequent breakdowns, inadequate maintenance schedules, and delayed rehabilitation processes often hinder the performance of such facilities. This study investigates the application of an Artificial Neural Network (ANN)-based system to improve the rehabilitation and maintenance of workshop equipment at Caritas University, Enugu. The proposed system leverages the predictive capabilities of ANN to monitor equipment usage, diagnose faults, and recommend optimal maintenance schedules. By analyzing historical maintenance data, operational parameters, and real-time feedback, the system aims to minimize downtime, reduce maintenance costs, and extend the equipment's lifespan. A case study approach was employed, integrating the ANN-based system into the university’s existing maintenance framework. Preliminary results indicate significant improvements in fault detection accuracy, response time, and resource allocation. This research highlights the potential of intelligent systems to revolutionize workshop maintenance practices, ensuring the sustainability and reliability of educational infrastructure.