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???This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." ???Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.???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

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A method for realizing wavelet transform (WT) is presented, in which the WT is synthesized by a bank of switched-current (SI) filters whose impulse responses are the basic wavelet function and its dilations. SI circuits are well suitable for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the sampling frequency. In this article, the wavelet base is approximated by a systematic algorithm with all the involved approximation parameters taken into account. Also, the SI filter employing the followthe-leader feedback (FLF) multiple-loop feedback (MLF) structure is proposed to synthesize the approximation function. The Gaussian wavelet is selected as an example to illustrate the design procedure. Simulation results indicate that the proposed method has the merits of high approximation accuracy, strong stability and low sensitivity.