Multi-class fault diagnosis based on support vector machines with sequenced binary tree archtecture

If classifiers of a support vector machine with binary tree architecture are arrayed randomly in the binary tree,their performance is not the best.A sequenced method in consideration of the sample range was proposed to rationally array classifiers of a support vector machine with binary tree architecture.A sample distribution radius and a sample distribution distance were introduced to estimate sample range of all classes in high-dimension characteristic space.The classes with bigger sample range were classified earlier in the higher nodal point of the binary tree architecture,and were given wider classificatory areas in the characteristic space.The experiment of multi-class fault diagnosis of a rotor showed that the proposed method distinctly improves the fault recognition accuracy,the diagnosis speed and the generalization,and it is suitable for practical application of multi-class fault diagnosis.