中文摘要：

为了高速、高效的测试和诊断模拟电路，提出一种将局部均值分解（LMD）多尺度熵和极限学习机相结合的模拟电路故障诊断的新方法。该方法中，首先采用LMD将故障信号分解为若干个乘积函数（production function，PF）；然后，求出各PF分量的多尺度熵并构造故障特征向量；最后，将特征向量输入到极限学习机中进行训练和测试。仿真实验结果显示采用该方法诊断时间只需0．02874S，诊断精度达到了98．89％。相较于其他3种方法有效减少诊断时间，提高故障诊断精度。

英文摘要：

In order to efficiently test and high speed diagnose analog circuits, a new analog circuit fault diagnosis method based on LMD multi-scale entropy and extreme learning machine is proposed in this paper. First, the fault signal is decomposed into several production functions by LMD. Then, the muhi-scale entropy of each PF component is worked out and fault feature vectors are constructed. Finally, the fault feature vectors are feed into the extreme learning machine to train and test. The simulation results show that the diagnosis time only needs O. 028 74 s, and the accuracy of fault diagnosis can achieve 98.89%. Compared with other three ways, the method can effectively reduce the diagnosis time and improve the accuracy of fault diagnosis.