Fault diagnosis using binary tree and sphere-structured support vector machines

A new method (BSSVM) using binary tree and sphere-structured support vector machines (SSVM) is presented for fault diagnosis. It constructs the hyper-planes step by step according to binary tree, partitions a class in every step and eliminates blind areas which are not insuperable for other multi-class methods of SVM. 4 common faults are created by a test-bed of rotor, their vibration signals are collected and transformed to frequency domain by FFT, then the spectrum energy in 8 bands divided by their total energy are taken as the energy distributions. With PCA, the 8-dimensional energy distributions are converted to 2-dimensional fault samples which can hold more than 80% useful information of the primary data. With the fault samples, the new method is tested and compared with several other multiclass methods of SVM, and experimental results show that the new method has higher speed and better accuracy than other similar ones.