Multifractal Analysis for Soft Fault Feature Extraction of Nonlinear Analog Circuits

Abstract

Aiming at the nonstationarity and nonlinearity of soft fault signals of nonlinear analog circuits, the use of multifractal detrended fluctuation analysis can effectively reveal the dynamic behavior hidden in multiscale nonstationary signals. This paper adopts a new method that uses multifractal detrended fluctuation analysis to calculate the multifractal singularity spectrum of soft fault signals of nonlinear analog circuits. Moreover, this method endows the parameters of the spectrum with definite physical meanings including width, maximum singular index, minimum singular index, and corresponding singularity index of the extreme point. Therefore, this method can be applied to characterize the internal dynamic mechanism of the soft fault signals of nonlinear analog circuits, making it suitable for the feature extraction of fault circuits. All multifractal feature parameters can be organized into a feature set, which will be then input to a support vector machine, and fault detection for the nonlinear analog circuit can be conducted via the support vector machine.