A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor

Abstract

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.