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Machine Learning-Based Maintenance Need Prediction for a Power Station Case Study
Abstract
This chapter presents the development and application of a machine learning-based maintenance need prediction system using regression models, focusing on a coal-fired thermal power station in Southern Africa. This study emerged from the need to address the inefficiencies in traditional maintenance practices at the power station, where the aging infrastructure has led to frequent equipment failures, costly repairs, and significant downtime. The chapter explores the steps taken to develop a predictive maintenance model using operational data from the power station, the selection and tuning of machine learning models, and the practical implications of deploying such a system in an industrial setting. The machine learning model demonstrated a high level of accuracy in predicting exceedance counts, which are critical indicators of potential equipment failures meaning that predictive maintenance system could effectively reduce downtime and prevent costly repairs.
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