关键词: 故障检测, 非平衡数据, 过采样, 代价敏感, 相关向量机

Abstract: Aimming at the problems of complicated mechanism of fault and lack of faulty samples in fault detection of electronic equipment, a fault detection method based on Safe Level Synthetic Minority Oversampling TEchnique（SL-SMOTE） and Cost Sensitive Relevance Vector Machine（CS-RVM） is proposed. The proposed method considers the fault detection of electronic equipment as an imbalanced binary-class classification problem. Firstly, SL-SMOTE is used in data layer to expand the fault samples. Then RVM detector is trained by these optimized samples. At last, cost sensitive learning is introduced to the detection in order to get the results at the minimun cost of loss. The experimental results of the UCI dataset and application case show that the proposed method effectively improves the detection accuracy.

Key words: fault detection, imbalanced data, oversampling, cost sensitive, relevance vector machine