Defect classification using PEC respones based on power spectral density analysis combined with EMD and EEMD
Title
Defect classification using PEC respones based on power spectral density analysis combined with EMD and EEMD
Subject
Defect classification
Empirical mode decomposition
Ensemble empirical mode decomposition
Power spectrum density analysis
Pulsed eddy current
Description
The defect classification is investigated by using features-based giant-magnetoresistive pulsed eddy current (GMR-PEC) sensor. The power spectrum density of the intrinsic mode functions (IMFs) is extracted as the classification feature, considering the disadvantage of selecting a wavelet base determined in previous work on spectral analysis combined with wavelet-decomposition. The IMFs are derived through empirical mode decomposition (EMD) and ensemble EMD. Principal component analysis, linear discriminant analysis, and Bayesian classifier are employed for defect classification algorithm. The proposed approach is validated by experiments, and results indicate that the cracks and cavities in the surface and subsurface can be classified satisfactorily.
37-51
78
Creator
Peng, Ying
Qiu, Xuanbing
Wei, Jilin
Li, Chuanliang
Cui, Xiaochao
Publisher
NDT & E International
Date
2016
Type
journalArticle
Identifier
0963-8695
10.1016/j.ndteint.2015.11.003
Collection
Citation
Peng, Ying et al., “Defect classification using PEC respones based on power spectral density analysis combined with EMD and EEMD,” Lamar University Midstream Center Research, accessed May 13, 2024, https://lumc.omeka.net/items/show/27513.