Development of a Predictive Maintenance Algorithm for a Diesel Generator using Machine Learning

This study develops a predictive maintenance framework for a 500kVA diesel generator using advanced machine learning techniques, aiming to enhance reliability and operational efficiency. The research involves the collection of real- world operational data at one-minute intervals over two months, focusing on critical parameters such as bearing temperature, engine vibration, and coolant temperature. Two machine learning models—XGBoost and Multi-Layer Perceptron (MLP)—were trained to classify generator conditions into distinct maintenance categories with high accuracy. A meta-learning ensemble approach was implemented, integrating the predictions from these models to leverage their complementary strengths and enhance robustness. The results demonstrate exceptional performance, with both individual and ensemble models achieving precision, recall, and F1-scores near 1.00 across multiple fault scenarios. The meta-learning framework proved particularly effective, showcasing improved reliability over standalone models. This study’s contributions are twofold: it advances the state of predictive maintenance by employing hybrid modelling techniques and addresses a critical gap in the proactive management of high-capacity diesel generators. The research underscores the practical applicability of machine learning in industrial contexts, offering a scalable and sustainable solution to minimise downtime, reduce maintenance costs, and optimise equipment longevity. By integrating robust data analysis with cutting- edge machine learning, this framework establishes a foundation for proactive, data-driven maintenance strategies in industrial settings, aligning with the broader goals of Industry 4.0 and sustainable industrial practices.