A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor
A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor
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DOI：
10.1109/tim.2009.2025068
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发表时间：
2010-03-01
影响因子：
5.6
通讯作者：
Sun, Yichuang
中科院分区：
文献类型：
--
作者：
Yuan, Lifen;He, Yigang;Sun, Yichuang
本文提出了一种新的模拟电路故障诊断方法。该方法通过数据采集板从被测电路的输出端提取原始信号，求出信号的峰度和熵，并利用峰度和熵度量信号的高阶统计量。然后将熵和峰度馈送到神经网络作为进一步故障分类的输入。该方法通过对模拟电路输出信号的分析，可以准确地检测和识别模拟电路中的故障元件，适用于非线性电路。基于信号峰度和熵的神经网络分类器预处理简化了网络结构，减少了训练时间，提高了网络性能。结果表明，当故障元件的响应值从零变化到无穷大时，熵和峰的次摆线是唯一的，因此，当响应不重叠时，可以正确识别故障元件。将该方法应用于三个线性和非线性电路，所实现的故障识别的平均准确率超过99%，虽然有一些重叠的数据时，考虑容差。此外，当故障元件开路时，所有的次摆线都收敛到一点，因此，该方法不仅可以分类软故障，也可以分类硬故障。
This paper presents a new fault diagnosis method for analog circuits. The proposed method extracts the original signals from the output terminals of the circuits under test (CUTs) by a data acquisition board and finds the kurtoses and entropies of the signals, which are used to measure the high-order statistics of the signals. The entropies and kurtoses are then fed to a neural network as inputs for further fault classification. The proposed method can detect and identify faulty components in an analog circuit by analyzing its output signal with high accuracy and is suitable for nonlinear circuits. Preprocessing based on the kurtosis and entropy of signals for the neural network classifier simplifies the network architecture, reduces the training time, and improves the performance of the network. The results from our examples showed that the trochoid of the entropies and kurtoses is unique when the faulty component's value varies from zero to infinity; thus, we can correctly identify the faulty components when the responses do not overlap. Applying this method for three linear and nonlinear circuits, the average accuracy of the achieved fault recognition is more than 99%, although there are some overlapping data when tolerance is considered. Moreover, all the trochoids converge to one point when the faulty component is open-circuited, and thus, the method can classify not only soft faults but also hard faults.