Emergency valve fault location based on improved optimal binary tree support vector machine

This paper presents a new algorithm IOBT SVM (improved optimal binary tree support vector machine) to locate the fault of the passenger train emergency valve and improve the test efficiency of the brake system. To reduce the classification classes and identify different faults, every two carriage faults are merged into one class and multiple sets of air pressure curve characteristics are extracted in sections. Furthermore, the structure of the classification tree is constructed from the leaf nodes to the root node combining with the class separability between every two classes. Every two most similar classes are selected in turn for classification and will be merged into one class after classification until the last class is left. Experimental results show that the structure of the classification tree generated by this algorithm is reasonable, which improves the efficiency and accuracy of emergency valve fault location.