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Soft Fault Diagnosis of Analog Circuit Based on EEMD and Improved MF-DFA

Authors :
Xinmiao Lu
Zihan Lu
Qiong Wu
Jiaxu Wang
Cunfang Yang
Shuai Sun
Dan Shao
Kaiyi Liu
Source :
Electronics; Volume 12; Issue 1; Pages: 114
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

Aiming at the problems of nonlinearity and serious confusion of fault characteristics in analog circuits, this paper proposed a fault diagnosis method for an analog circuit based on ensemble empirical pattern decomposition (EEMD) and improved multifractal detrended fluctuations analysis (MF-DFA). This method consists of three steps: preprocessing, feature extraction, and fault classification identification. First, the EEMD decomposition preprocesses (denoises) the original signal; then, the appropriate IMF components are selected by correlation analysis; then, the IMF components are processed by the improved MF-DFA, and the fault feature values are extracted by calculating the multifractal spectrum parameters, and then the feature values are input to a support vector machine (SVM) for classification, which enables the diagnosis of soft faults in analog circuits. The experimental results show that the proposed EEMD-improved MF-DFA method effectively extracts the features of soft faults in nonlinear analog circuits and obtains a high diagnosis rate.

Details

Language :
English
ISSN :
20799292
Database :
OpenAIRE
Journal :
Electronics; Volume 12; Issue 1; Pages: 114
Accession number :
edsair.doi.dedup.....18e4e9b8b8b453644aee45ea6cb49ac0
Full Text :
https://doi.org/10.3390/electronics12010114