A novel fault diagnosis approach combining SVM with association rule mining for ship diesel engine

摘要

In this paper, a novel fault diagnosis approach combining support vector machine (SVM) with association rule mining for ship diesel engine is designed for enhancing accuracy rate of ship diesel engine fault diagnosis. We used SVM algorithm and association rule to analyze fault data for lubricating subsystem of ship diesel engine so as to achieve fault diagnosis for lubricating system. We explained in detail fault diagnosis for lubricating system in the paper. Finally, we design a ship diesel engine condition monitoring and fault diagnosis simulation system used to verify the novel fault diagnosis approach.