Application of empirical mode decomposition in early diagnosis of magnetic memory signal

Jian-cheng Leng , Min-qiang Xu , Jia-zhong Zhang

Journal of Central South University ›› 2010, Vol. 17 ›› Issue (3) : 549 -553.

PDF
Journal of Central South University ›› 2010, Vol. 17 ›› Issue (3) : 549 -553. DOI: 10.1007/s11771-010-0521-5
Article

Application of empirical mode decomposition in early diagnosis of magnetic memory signal

Author information +
History +
PDF

Abstract

In order to eliminate noise interference of metal magnetic memory signal in early diagnosis of stress concentration zones and metal defects, the empirical mode decomposition method combined with the magnetic field gradient characteristic was proposed. A compressive force periodically acting upon a casing pipe led to appreciable deformation, and magnetic signals were measured by a magnetic indicator TSC-1M-4. The raw magnetic memory signal was first decomposed into different intrinsic mode functions and a residue, and the magnetic field gradient distribution of the subsequent reconstructed signal was obtained. The experimental results show that the gradient around 350 mm represents the maximum value ignoring the marginal effect, and there is a good correlation between the real maximum field gradient and the stress concentration zone. The wavelet transform associated with envelop analysis also exhibits this gradient characteristic, indicating that the proposed method is effective for early identifying critical zones.

Keywords

metal magnetic memory / noise interference / early diagnosis / empirical mode decomposition / magnetic field gradient / stress concentration / zones / envelop analysis

Cite this article

Download citation ▾
Jian-cheng Leng, Min-qiang Xu, Jia-zhong Zhang. Application of empirical mode decomposition in early diagnosis of magnetic memory signal. Journal of Central South University, 2010, 17(3): 549-553 DOI:10.1007/s11771-010-0521-5

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

PrestonC. R.. Magnetic particle inspection of precipitation hardening type steels [J]. Insight, 2002, 44(12): 782-785

[2]

LernerY., BrestelP.. Ultrasonic testing predicts casting properties [J]. Advanced Materials & Processes, 1996, 150(5): 39-41

[3]

DubovA. A.. A study of metal properties using the method of magnetic memory [J]. Metal Science and Heat Treatment, 1997, 39(9/10): 401-405

[4]

WilsonJ. W., TianG. Y., BarransS.. Residual magnetic field sensing for stress measurement [J]. Sensors and Actuators A, 2007, 135(2): 381-387

[5]

DongL.-h., XuB.-s., DongS.-y., ChenQ.-z., WangD.. Variation of stress-induced magnetic signals during tensile testing of ferromagnetic steels [J]. NDT & E International, 2008, 41(3): 184-189

[6]

YangE., LiL.-m., ChenX.. Magnetic field aberration induced by cycle stress [J]. Journal of Magnetism and Magnetic Materials, 2007, 312(1): 72-77

[7]

DongL.-h., XuB.-s., DongS.-y., ChenQ.-z., WangY.-y., ZhangL., WangD., YinD.-wei.. Metal magnetic memory testing for early damage assessment in ferromagnetic materials [J]. Journal of Central South University of Technology, 2005, 12(S2): 102-106

[8]

DongL.-h., XuB.-s., DongS.-y., YeM.-h., ChenQ.-z., WangD., YinD.-wei.. Metal magnetic memory signals from surface of low-carbon steel and low-carbon alloyed steel [J]. Journal of Central South University of Technology, 2007, 14(1): 24-27

[9]

LengJ.-c., XuM.-q., XuM.-x., ZhangJ.-zhong.. Magnetic field variation induced by cyclic bending stress [J]. NDT & E International, 2009, 42(5): 410-414

[10]

DubovA. A.. The method of metal magnetic memory-The new trend in engineering diagnostics [J]. Welding in the World, 2005, 49(S): 314-319

[11]

ShaoX.-w., ZhangJ.. Study on wavelet analysis in metal magnetic memory forecasts the fault of borehole casing well [C]. Proceedings of the 6th World Congress on Intelligent Control and Automation, 2006, Piscataway, Institute of Electrical and Electronics Engineers Inc: 5557-5561

[12]

ZhangJ., WangB., JiB.-yu.. Signal processing for metal magnetic memory testing of borehole casing based on wavelet transform [J]. Acta Petrolei Sinica, 2006, 27(2): 137-140

[13]

ZhangJ., WangB.. Recognition of signals for stress concentration zone in metal magnetic memory tests [J]. Proceedings of the CSEE, 2008, 28(8): 144-148

[14]

MaoY.-m., QueP.-wen.. Application of Hilbert-Huang signal processing to ultrasonic non-destructive testing of oil pipelines [J]. Journal of Zhejiang University: Science A, 2005, 7(2): 130-134

[15]

HuangN. E., ShenZ., LongS. R., WuM. C., ShihH. H., ZhengQ., YenN. C., TungC. C., LiuH. H.. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society of London A, 1998, 454: 903-995

[16]

PengZ. K., PeterW. T., ChuF. L.. An improved Hilbert-Huang transform and its application in vibration signal analysis [J]. Journal of Sound and Vibration, 2005, 286(1/2): 187-205

AI Summary AI Mindmap
PDF

131

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/