Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

doi:10.1007/s12204-015-1691-y

上海交通大学学报（英文版） ›› 2016, Vol. 21 ›› Issue (1): 121-128.doi: 10.1007/s12204-015-1691-y

• • 上一篇

Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

ZHU Hongyun (朱红运), WANG Changlong* (王长龙), CHEN Hailong (陈海龙), WANG Jianbin (王建斌)

(Department of Unmanned Aerial Vehicles Engineering, Ordnance Engineering College, Shijiazhuang 050003, China)

出版日期:2016-02-29 发布日期:2016-03-21
通讯作者: WANG Changlong* (王长龙) E-mail: wang-oec@126.com

Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

ZHU Hongyun (朱红运), WANG Changlong* (王长龙), CHEN Hailong (陈海龙), WANG Jianbin (王建斌)

(Department of Unmanned Aerial Vehicles Engineering, Ordnance Engineering College, Shijiazhuang 050003, China)

Online:2016-02-29 Published:2016-03-21
Contact: WANG Changlong* (王长龙) E-mail: wang-oec@126.com

摘要/Abstract

摘要： The noise as an undesired phenomenon often appears in the pulsed eddy current testing (PECT) signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the PECT signal is the Gaussian noise, since it is caused by the testing environment. A new denoising approach based on singular value decomposition (SVD) is proposed in this paper to reduce the Gaussian noise of PECT signal. The approach first discusses the relationship between signal to noise ratio (SNR) and negentropy of PECT signal. Then the Hankel matrix of PECT signal is constructed for noise reduction, and the matrix is divided into noise subspace and signal subspace by a singular valve threshold. Based on the theory of negentropy, the optimal matrix dimension and threshold are chosen to improve the performance of denoising. The denoised signal Hankel matrix is reconstructed by the singular values of signal subspace, and the denoised signal is finally extracted from this matrix. Experiment is performed to verify the feasibility of the proposed approach, and the results indicate that the proposed approach can reduce the Gaussian noise of PECT signal more effectively compared with other existing approaches.

关键词: pulsed eddy current testing (PECT), singular value decomposition (SVD), negentropy, denoising

Abstract: The noise as an undesired phenomenon often appears in the pulsed eddy current testing (PECT) signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the PECT signal is the Gaussian noise, since it is caused by the testing environment. A new denoising approach based on singular value decomposition (SVD) is proposed in this paper to reduce the Gaussian noise of PECT signal. The approach first discusses the relationship between signal to noise ratio (SNR) and negentropy of PECT signal. Then the Hankel matrix of PECT signal is constructed for noise reduction, and the matrix is divided into noise subspace and signal subspace by a singular valve threshold. Based on the theory of negentropy, the optimal matrix dimension and threshold are chosen to improve the performance of denoising. The denoised signal Hankel matrix is reconstructed by the singular values of signal subspace, and the denoised signal is finally extracted from this matrix. Experiment is performed to verify the feasibility of the proposed approach, and the results indicate that the proposed approach can reduce the Gaussian noise of PECT signal more effectively compared with other existing approaches.

Key words: pulsed eddy current testing (PECT), singular value decomposition (SVD), negentropy, denoising

中图分类号:

TN 911.4

ZHU Hongyun (朱红运), WANG Changlong* (王长龙), CHEN Hailong (陈海龙), WANG Jianbin (王建斌). Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition[J]. 上海交通大学学报（英文版）, 2016, 21(1): 121-128.

ZHU Hongyun (朱红运), WANG Changlong* (王长龙), CHEN Hailong (陈海龙), WANG Jianbin (王建斌). Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition[J]. Journal of shanghai Jiaotong University (Science), 2016, 21(1): 121-128.

参考文献 19

[1]	XU Z Y,WU X J, LI J, et al. Assessment of wall thinningin insulated ferromagnetic pipes using the timeto-peak of differential pulsed eddy current testing signals[J]. NDT & E International, 2012, 51: 24-29.
[2]	ALAMIN M, TIAN G Y, ANDREWS A, et al. Principalcomponent analysis of pulsed eddy current responsefrom corrosion in mild steel [J]. IEEE SensorsJournal, 2012, 12(8): 2548-2553.
[3]	ANGANI C S, PARK D G, KIM C G, et al. Thepulsed eddy current differential probe to detect a thicknessvariation in an insulated stainless steel [J]. Journalof Nondestructive Evaluation, 2010, 29: 248-252.
[4]	CHENG W Y. Pulsed eddy current testing of carbonsteel pipes wall-thinning through insulation andcladding [J]. Journal of Nondestructive Evaluation,2012, 31: 215-224.
[5]	MUKRIZ I, TIAN G Y, LI Y. 3D transient magneticfield mapping for angular slots in aluminium [J].Insight, 2009, 51(1): 21-24.
[6]	KIWA T, HAYASHI T, KAWASAKI Y, et al. Magneticthickness gauge using a Fourier transformed eddycurrent technique [J]. NDT & E International, 2009,42: 606-609.
[7]	HE Y Z, LUO F L, PAN M C. Defect characterizationbased on pulsed eddy current imaging technique[J]. Sensors and Actuators A, 2010, 164: 1-7.
[8]	HOSSEINI S, LAKIS A A. Application of time frequencyanalysis for automatic hidden corrosion detectionin a multilayer aluminum structure using pulsededdy current [J]. NDT & E International, 2012, 47:70-79.
[9]	THEODOULIDIS T, WANG H T, TIAN G Y. Extensionof a model for eddy current inspection of cracksto pulsed excitations [J]. NDT & E International, 2012,47: 144-149.
[10]	FAN M B, HUANG P J, YE B, et al. Analyticalmodeling for transient probe response in pulsed eddycurrent testing [J]. NDT & E International, 2009, 42:376-383.
[11]	HUANG C, WU X J, XU Z Y, et al. Pulsed eddycurrent signal processing method for signal denoisingin ferromagnetic plate testing [J]. NDT & E International,2010, 43: 648-653.
[12]	RENINGER P A, MARTELET G, DEPARIS J, etal. Singular value decomposition as a denoising tool forairborne time domain electromagnetic data [J]. Journalof Applied Geophysics, 2011, 75: 264-276.
[13]	RAJWADE A, RANGARAJAN A, BANERJEEA. Image denoising using the higher order singularvalue decomposition [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2013, 35(4):849-862.
[14]	ZHAO X Z, YE B Y. Similarity of signal processingeffect between Hankel matrix based SVD and wavelettransform and its mechanism analysis [J]. MechanicalSystems and Signal Processing, 2009, 23: 1062-1075.
[15]	ZHANG L Z. Surface defects inspection for continuouscasting slab by pulsed eddy current [D]. Chongqing,China: College of Material Science and Engineering,Chongqing University, 2011 (in Chinese).
[16]	HASSANPOUR H, ZEHTABIAN A, SADATI S J.Time domain signal enhancement based on an optimizedsingular vector denoising algorithm [J]. DigitalSignal Processing, 2012, 22: 786-794.
[17]	SHIH Y T, CHIEN C S, CHUANG C Y. Anadaptive parameterized block based singular value decompositionfor image denoising and compression [J].Applied Mathematics and Computation, 2012, 218:10370-10385.
[18]	LAGO-FERN′ANDEZ L F, S′ANCHEZMONTA?N′ES M, CORBACHO F. The effectof low number of points in clustering validation viathe negentropy increment [J]. Neurocomputing, 2011,74: 2657-2664.
[19]	TIAN G Y, HE Y Z, ABEWALE I, et al. Researchon spectral response of pulsed eddy current and NDEapplications [J]. Sensors and Actuators A, 2013, 189:313-320.

Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

Pulsed Eddy Current Signal Denoising Based on Singular Value Decomposition

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