PCA-SVM在模拟电路故障诊断中的应用

Application of PCA-SVM in Fault Diagnosis for Analog Circuit

主元分析法(PCA)通过提取故障样本集的主元得到降维的特征空间,利于故障特征提取;支持向量机(SVM)应用于故障诊断时具有良好分类性能;结合两者优点,提出了基于PCA特征提取和SVM相结合的模拟电路故障诊断识别新方法:对电路输出响应信号进行PCA处理,提取故障特征的主成分,然后利用多类SVM对故障模式进行分类决策,实现故障诊断;仿真实验结果表明,该方法能够实现模拟电路故障的快速检测与故障定位,具有速度快、精度高、鲁棒性好的特点。

Principal Components Analysis （PCA） extract the main element from the fault sample set to obtain compacted feature space so it is propitious for fault diagnosis. Support Vector Machine （SVM） has shown its good classification performance in fault diagnosis. A new method of fault diagnosis for analog circuit based on PCA--SVM is raised and it includes both advantages. The circuit output is sampled in frequency domain and it is preprocessed by PCA to extract main components of the fault features. Fault patterns under various states are classified using multi--class SVM, and fault diagnosis is realized. The simulation results show that PCA--SVM is feasible to detect and locate faults quickly and exactly and has high robustness.