Rehabilitation And Maintenance Of Workshop Equipments Using Convolutional Neural Network (Cnn). a Case Study Of Safety In Caritas University Workshop Enugu

Abstract

The effective rehabilitation and maintenance of workshop equipment are crucial for ensuring operational efficiency and safety in academic and industrial environments. This study explores the application of Convolution Neural Networks (CNN) in the rehabilitation and maintenance of workshop equipment at Caritas University Workshop, Enugu, with a focus on enhancing safety protocols. The primary aim of the research is to develop a predictive maintenance system that can detect potential equipment failures and prevent safety hazards through real-time data analysis. A CNN model was trained using sensor data and images from the workshop equipment to identify anomalies such as unusual vibrations, temperature fluctuations, and wear and tear that may signal impending failure. The results demonstrated the model's ability to accurately predict equipment failures, allowing for timely maintenance interventions and reducing downtime. Additionally, the system significantly contributed to improving safety by detecting unsafe operating conditions before they led to accidents. The study found that integrating AI-driven predictive maintenance and safety protocols could optimize workshop operations, increase equipment lifespan, and enhance the overall safety of workshop environments. This research provides valuable insights into the potential of AI technologies, particularly CNNs, to revolutionize maintenance practices and safety management in educational and industrial settings. Future work should focus on expanding the dataset, optimizing computational resources, and exploring the scalability of the model for broader industrial applications.