Abstract: To solve the overlap of part of fault classes in the analog circuit fault diagnostics, a novel analog circuit fault diagnostics approach based on quantum neural networks algorithm was presented. Kurtosis and entropy were calculated as features after the time domain response signals of the circuit under test were measured, and then the different fault classes were identified by quantum neural networks algorithm. The simulation demonstrated that constructed neural network had simple network structure and the fault diagnosis accuracy was higher, which reached 99.62%.

Key words: Analog circuit, Fault diagnostics, Kurtosis, Entropy, Quantum neural networks

摘要：  针对模拟电路中部分故障类别发生重叠的特点, 提出了一种基于量子神经网络算法的模拟电路故障诊断方法。在被测电路输出端采集时域响应信号, 计算其峭度和熵, 作为特征量, 并应用量子神经网络算法对模拟电路的各个不同的故障类别进行辨别。实验结果表明, 构建的神经网络具有简单的网络结构, 且故障诊断正确率较
高, 达到9 9. 6 2%。

关键词: 模拟电路, 故障诊断, 峭度, 熵, 量子神经网络