Journal Article

Research on Analog Circuit Soft Fault Diagnosis Method Based on Mathematical Morphology Fractal Dimension

Wu, Qiong, Liu, Kaiyi, Lu, Zihan, Yang, Cunfang, Wang, Jiaxu, Shao, Dan, Lu, Xinmiao, Sun, Shuai

2023

Overview

It is difficult for traditional circuit-fault feature-extraction methods to accurately distinguish between nonlinear analog-circuit faults and analog-circuit faults with high fault rates and high diagnostic costs. To solve this problem, this paper proposes a method of mathematical morphology fractal dimension (VMD-MMFD) based on variational mode decomposition for soft-fault feature extraction in analog circuits. First, the signal is decomposed into variational modes to suppress the influence of environmental noise, and multiple high-dimensional eigenmode functions with different center frequencies are obtained. The fractal dimension of the signal feature information component IMF is calculated, and then, KPCA (Kernel Principal Component Analysis) is used to remove the overlapping and redundant parts of the data. The fault set obtained is used as the basis for judging the working state and the fault type of the circuit. The experimental results of the simulation circuits show that this method can be effectively used for circuit-fault diagnosis.

• Publisher: MDPI AG
• Subject: Algorithms / Analog circuits / Analog integrated circuits / Background noise / Circuits / Corrosion / Decomposition / Eigenvalues / Eigenvectors / Electric fault location / Fault diagnosis / Feature extraction / Fractal geometry / Fractals / Mathematical analysis / Mathematical morphology / Methods / Morphology / Principal components analysis / Signal processing