基于沃尔泰拉级数的模拟电路组合故障诊断法

针对模拟电路的固有复杂性及其传统故障检测方法延时大和正确识别率低的问题,借鉴基于隐马尔科夫模型改进最小二乘支持向量机以及Volterra级数原理,将二者组合进行故障诊断。该方法首先采用Volterra级数频域核对电路故障特征进行提取,再利用经隐马尔科夫模型改进的最小二乘支持向量机进行模态分类,最终完成故障诊断。仿真结果表明,与目前使用的BP神经网络诊断方法和LSSVM诊断方法相比,该方法不仅提高了系统故障辨识能力,还提高了系统故障诊断的速度。

Combinational method for fault diagnosis in analog circuits based on Volterra series

In order to solve the problems of inherent complexity of analog circuits,long detecting time and low correct recognition rate in traditional fault diagnosis methods, we propose a combinational method for fault diagnosis in analog circuits, which combines improved Least Squares Support Vector Machine (LS SVM) by Hidden Markov Model（HMM）with Volterra series.Firstly,we use frequency domain core of Volterra series to extract circuit fault features.Then we adopt improved LS SVM by HMM for modal classification,and the fault diagnosis is completed.Simulation results show that compared with traditional BP neural network and LSSVM method,the proposed method is more efficient in system fault identification and faster in fault diagnosis.