基于PCA-LVQ的模拟电路故障诊断

Analog circuit fault diagnosis based on PCA and LVQ

为了解决模拟电路故障难于识别的问题,提出一种基于主成分分析(PCA)和学习矢量量化神经网络(LVQ)的模拟电路故障诊断新方法。该方法用PCA提取模拟电路故障特征,然后将降维后的故障特征信息输入LVQ网络训练和故障模式的分类识别。通过对Sallen-Key带通滤波器电路的故障诊断实例表明,该方法是有效的,具有较高的故障诊断率。

In order to solve the difficulty of recognition in analog circuit fault diagnosis,a new analog circuit fault diagnosis method based on principal component analysis（PCA） and learning vector quantization（LVQ） is proposed in this paper.PCA is applied to extract the feature of the response signals to circuit under test（CUT）.Then the optimal feature is inputted into an LVQ network to train and identify different fault cases.The example of Sallen-Key bandpass filter circuit fault diagnosis shows that this method is effective and has higher fault diagnosis rate.