Marine Diesel Engine Fault Diagnosis Based On Support Vector Machines

SVM(Support Vector Machine),which is based on N.Vapnik's Statistical Learning Machine Theory,now is the most advanced machine learning algorithm in the field of the pattern recognition ,and its characters have already showed more superiority than other methods.it can solve small sample learning problems better by using Structural Risk Minimization than Empirical Risk Minimization.Moreover,by using the Kernel function idea.this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity.SVM have become the hot spot of machine learning because of their excellent learning performance. They also have successful applications in many fields ,.but as a new technique,SVM also have many shortcomings that need to be tracked and bettered ,including:the adaptive kernel and parameter selection,the shortcomings of training methods and incremental learing ,etc.becauseof these problems ,the applications of SVM are limited in many fields.In this paper ,the author discusses the applications of SVM in the fault diagnosis of fuel oil system of marine main engine.The author carries on simulation research and expeeriment research of fault diagonosis by using SVM theory,including :design of the mathematics model,simulation model(by simulink),extraction of the feature parameter and data collection of the experimental system.