Fault detection based on SVDD and cluster algorithm
Jing Yu Xu, Jun Yao, Lan Ni · 2011
As abnormal samples are difficult to obtain in the field of fault detection, a fault detection model based on support vector data description (SVDD) algorithm and Cluster algorithm is presented in this paper. It is the improvement of traditional SVDD algorithm. Firstly, K-MEANS classification method is used to cluster the normal bearing vibration signal samples. Then SVDD algorithm is applied to describe the clustered data distribution. It can make up the defect that the training samples are not concentrated so the traditional SVDD contains non-self space samples causing the poor description. ROC criterion is used to compare with the other commonly used fault detection method. The experiment results verify the correctness and effectiveness of the algorithm and the method is especially for fault detection application.