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This research paper presents a novel bi-phase framework for fault detection and diagnosis (FDD) in diesel engine systems, utilizing a dataset and traditional machine learning techniques. The proposed model achieves 100% accuracy in both detection and diagnosis phases, significantly improving convergence speed and efficiency by integrating outputs from six tree-based classifiers. This study addresses gaps in existing literature by offering a comprehensive approach to multiple fault types across various engin

Author

saciokki

Language

EN