Research and Application of Anormaly Detection Based on Improved DM-SVDD Algorithm

Abstract

Aiming at the problem that traditional anomaly detection model has poor recognition effect on a small number of abnormal samples under the condition of data imbalance, in this paper we proposed a support vector data description algorithm combined with improved diffusion maps (DM-SVDD), constructed a new model and applied it to industry abnormal detection. The diffusion mapping algorithm was improved by introducing Euclidean distance and Mahalanobis distance to construct a new neighbor graph. Combined with support vector data description algorithm for modeling, the new model improved the recognition performance of normal samples, and the detection performance of abnormal samples was better than that from traditional models. Experimental data were selected of polysilicon ingot data sets. The results show that for an unbalanced data set formed by fewer abnormal samples, compared with traditional anomaly detection model, the model proposed in this paper can increase G-Mean optimally by 15.73% and F-Score optimally by 19.37%, which meet the requirements of industrial anomaly detection. The model can be used to guide the actual production process and reduce production costs.

Keywords

• support vector data description
• diffusion maps
• anomaly detection
• unbalanced data
• polysilicon ingots data