Hidden defect recognition based on the improved ensemble empirical decomposition method and pulsed eddy current testing

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• 作者：Baoling Liu^a ; ^{lingbaoliu@163.com} ; Pingjie Huang^b ; Xuan Zeng^a ; Zhinong Li^c
• 关键词：Improved ensemble empirical mode decomposition (IEEMD) ; Pulsed eddy current (PEC) ; Marginal spectrum(MS) ; Hidden defect
• 刊名：NDT & E International
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：86
• 期：Complete
• 页码：175-185
• 全文大小：1984 K
• 卷排序：86

文摘

This article presents a new approach for recognizing hidden defects in conductive structures based on the improved ensemble empirical decomposition (IEEMD) and the pulsed eddy current (PEC) testing technique. First, the ensemble empirical mode decomposition (EEMD) method is improved in terms of envelope fitting, end effects and false components. Then, feature vector composed of the principal marginal spectrum peaks are extracted by the help of Hilbert weighted frequencies and characteristic frequencies. It has been verified by simulation signals that the IEEMD method can obtain more accurate intrinsic mode function components (IMFs). The proposed feature extraction method may recognize various hidden defect in the rail specimen with satisfying accuracy and robustness to such disturbances as noise and liftoff.