Ship Diesel Engine Fault Diagnosis Based on the SVM and Association Rule Mining

摘要

Ship diesel engine's structure is complex, and its fault has high coupling. For this reason, we study ship diesel engine fault diagnosis from two aspects. First of all, we divided ship diesel engine system into four parts according its basic structure and fault features, the fuel system, the lubrication system, the intake and exhaust system and the cooling system, and then analyzed the fault features for the each subsystem respectively. We used the support vector machine (SVM) algorithm to classify fault data for each subsystem of ship diesel engine. So that, we could implement the fault diagnosis for the each subsystem. It reduced the complexity of the whole system fault diagnosis. Secondly, Ship diesel engine fault often occurs between different the subsystems, and the occurrence of a fault is often accompanied by other fault. We could solve this high coupling by using association rule mining and then found out the implicit association rules of the fault in whole system.