Developing AI-based Preventive Maintenance Model for BGI Ethiopia: Washing Head
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Date
2024-07
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Addis Ababa University
Abstract
.for implementing real-time monitoring and continuous modelupdates to sustain and enhance the benefits improve production processes and reduce operational costs.The study concludes with recommendations value of AI-driven preventive maintenance in the breweryindustry, suggesting broader applications to kegs but also enhances the reliability and efficiency of theproduction line. The findings demonstrate the This predictive approach not only minimizesdowntime by preventing the processing of faulty performance, accurately forecasting potential kegrejections and enabling preemptive maintenance actions. including AdaBoost, to predict the readinessstate of Head-4. The AdaBoost model exhibited superior generating synthetic data to augment thedataset, the study trained several machine learning models, integrating real-time data from theComputerized Maintenance Management System (CMMS) and maintenance model to improveoperational efficiency and reduce these false rejections. By substantial downtime andeconomic losses. The objective of this research is to develop a predictive particularly at the washingHead-4. These rejections, caused by steam supply inconsistencies, result in linedue to the frequent rejection of kegs by the Slimline Monobloc 50 filler machine,
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Keywords
Preventive maintenance, Machine learning, Ensemble learning, Synthetic data generation, Brewery industry.