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

被引:6
|
作者
Lu, Xinmiao [1 ]
Lu, Zihan [1 ]
Wu, Qiong [1 ,2 ]
Wang, Jiaxu [1 ]
Yang, Cunfang [1 ]
Sun, Shuai [1 ]
Shao, Dan [3 ]
Liu, Kaiyi [4 ]
机构
[1] Harbin Univ Sci & Technol, Sch Measurement Control Technol & Commun Engn, Harbin 150080, Peoples R China
[2] Heilongjiang Network Space Res Ctr, Harbin 150090, Peoples R China
[3] Harbin Vocat Coll Sci & Technol, Harbin 150399, Peoples R China
[4] Harbin Meteorol Bur, Harbin 150028, Peoples R China
关键词
ensemble empirical pattern decomposition (EEMD); multifractal; detrended fluctuations analysis (DFA); support vector machines (SVM); circuit fault diagnosis;
D O I
10.3390/electronics12010114
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:16
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