For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

中文翻译：

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基于广义频率响应函数和LSSVM分类器融合的非线性模拟电路故障诊断

针对非线性模拟电路的故障诊断，提出了一种基于广义频率响应函数（GFRF）和最小二乘支持向量机（LSSVM）分类器融合的新方法。正弦信号用作模拟电路的输入，然后，通过时域公式直接估算广义频率响应函数。避免了测量数据的离散傅里叶变换。在获得广义的频率响应函数后，选择GFRF的幅度作为故障特征参数。基于最小二乘支持向量机（LSSVM）的分类器融合算法用于故障识别。具有不同内核功能的两个LSSVM多故障分类器被构造为子分类器。分别进行了电阻电容（RC）电路和Sallen Key滤波器的故障诊断实验。结果表明，该电路的估计GFRFs是准确的，故障诊断方法可以获得较高的识别率。