Support Vector Machine for Mechanical Faults Diagnosis

Aiming at the difficulty that Support Vector Machine (SVM) model selection of classification algorithm affect classification accuracy, it research relevant factors that influence the precision of fault classifiers based on the typical fault data samples obtained by experimental setup of rotor-bearing systems. The results show that different SVM classifiers, in which different kernel functions and different kernel functions parameters are adopted, will influence the precision of fault classifiers in conditions that fault data samples is small. It can be conveniently applied to choose appropriate kernel functions and kernel functions parameters in engineering application.