The multi-classification algorithm combining an improved binary tree with SVM and its application of fault diagnosis

When binary tree SVM is used for multi-class fault diagnosis, inner-class distance or between-class distance is always used to decide the classification hierarchy, but these methods cannot take the comprehensive separability information between classes into account, which leads to decrease the accuracy of fault diagnosis easily, so an improved binary tree SVM method is proposed. Combining the separability of inner-class with the separability of between-class, a measurement formula is built, which is based on a principle, that is the same class is relatively clustered and the different classes have a relatively far distance is easier to classify. Then according to it, the classification hierarchy is decided. In the end, the new method is applied to fault diagnosis of Tennessee Eastman (TE) process, the experimental results show it has an excellent integrated performance in comparison to other methods based on SVM.