A New Engine Fault Diagnosis Model Based on Support Vector Machine

Abstract

In order to solve the problem of bad reliability in self-propelled gun engine fault diagnosis method based on single sensor information, a fault diagnosis model based on improved least squares support vector machine (LSSVM) is presented. In the model, the quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in fault diagnosis, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of diagnosis. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best diagnosis results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the LSSVM model.