Support Vector Machine Applications In The Marine Diesel Engine Fault Diagnosis

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 is a new universal learning method and its characters have already showed more superiority than other methods. It can solve small sample learning problems as well as non-linear,high dimension and local minimization problem etc. better by using Structural Risk Minimization than using Empirical Risk Minimization. Moreover, by using the Kernel function idea, this theory can change the problem in non-linear 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 learning ,etc. because of 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 turbocharger system of marine main engine of the ship . The author carries on simulation research and experiment research of fault diagnosis 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.