Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

Abstract

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.