样本不均衡条件下基于自调整支持向量机的故障诊断

故障数据样本和正常运行数据样本量的不均衡将导致支持向量机在构建故障分类超平面时发生偏移,降低了基于支持向量机的故障诊断的诊断准确率. 针对该问题,文中提出一种能够自动调整风险惩罚因子的新型支持向量机. 该方法能够自举式地对有效样本进行挑选,并加大高信息量数据样本的风险惩罚因子,抑制样本不均衡导致的分类超平面偏移,进而提高故障诊断的准确性. 所提方法被用于变压器故障诊断实验,实验过程中正负样本的风险损失始终相等,有效地抑制了样本不均衡现象对诊断造成的影响,验证了所提算法的有效性. 

Fault Diagnosis Based on Self-Tuning Support Vector Machine in Sample Unbalance Condition

The unbalance of sample quantities between faulty samples and normal samples leads to a deviation of classifying hyperplane while using support vector machine (SVM), hence decreases the accuracy of SVM-based fault diagnosis. A new self-tuning support vector machine (St-SVM), which could automatically adjust the penalty factors for risk function, is proposed for this issue. This method selects informative samples by booststrapping approach, amplifies their risk penalty factors and decreases the deviation of classification hyperplane brought by sample unbalance, and hence improves the accuracy of fault diagnosis. The St-SVM has been applied to the diagnosis of transformer faults. During the experiment, the positive and negative samples yield equal loss risks, and the diagnostic performance with unbalanced samples is significantly improved. It demonstrates the effectiveness of the proposed approach.