Abstract
The method of metal magnetic memory (MMM) was developed for early fault diagnosing of ferromagnetic materials. MMM signal is a weak-field detect signal, where the Earth’s magnetic field acts as the stimulus instead of an artificial magnetic field, and can be easily affected by the various factors such as environment interference and electronic noise. This paper is aimed to denoise metal magnetic memory signal and extract the feature of stress concentration zone. An efficient algorithm is proposed for detection of stress concentration zone based on wavelet and teager energy operator (TEO). This algorithm employs wavelet transform, to decompose the MMM signal into sub-band signal. In each of the critical sub-band signals, the mask construction is obtained by smoothing the TEO of corresponding wavelet coefficients that is applied to enhance the discriminability of signal components against those of noise. The multiscale related feature is extracted for the low signal-to-noise ratio signals that accurately determines the stress concentration. Finally, the proposed method is proved to be effective through the experimental data.
This work is supported by National Natural Science Foundation (51405303), Postdoctoral fund of Jiangsu Province (1301175C) Priority Academic Program Development of Jiangsu Higher Education Institutions.
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References
Doubov AA (1998) Screening of weld quality using the metal magnetic memory. Weld World 41:196–199
Dubov N, Sergey K (2013) The metal magnetic memory method application for online monitoring of damage development in steel pipes and welded joints specimens. Weld World 57:123–136
Xiao-meng L, Hong-sheng D, Shi-wu B (2014) Research on the stress-magnetism effect of ferromagnetic materials based on three-dimensional magnetic flux leakage testing. NDT & E Int 62(5):50–54
Sablik MJ, Wilhelmus JG, Smith K (2010) Modeling of plastic deformation effects in ferromagnetic thin films. IEEE Trans Magn 46(2):491–494
Jun Z, Biao W, Bingyu J (2006) Signal processing for metal magnetic memory testing of borehole casing based on wavelet transform. Acta Petrol Ei Sinica 27(2):137–140
Yang H, Yihua K, Wenxiang L, Shuzi Y (2000) Some algorithms for nondestructive testing of wire ropes—signal pre-processing and character extraction. Nondestr Test 22(11):483–488
Xiaoyong Z, Yinzhong Y (2004) Multi-fault diagnosis method based on Mallat pyramidal algorithms wavelet analysis. Control and Decis 19(5):592–594
Kaiser JF (1990) On a simple algorithm to calculate the energy of a signal, China Mechanical ICASSP. Academies Press, Albuquerque, pp 381–384
Chan TF, Hao-Min Z (2007) Total variation wavelet thresholding, J Sci Comput 32(2):315–341
Krommveh J, Jian-wei MA (2010) Tetrolet shrinkage with anisotropic total var-iation minimization for image approximation, Sig Process 90(8):2529–2539
Om H, Biswas M (2012) An improved image denoising method based on wave-let, J Sig Inf Process 3(1):109–116
Mallat SG, Hwang WL (1992) Singularity detection and processing with wave-lets, IEEE Trans IT 38(2):617–643
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© 2016 Springer Science+Business Media Singapore
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Zhu, S., Zhang, J., Bi, Z. (2016). Metal Magnetic Memory Signal Denoising for Stress Concentration Zone. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_28
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DOI: https://doi.org/10.1007/978-981-10-2338-5_28
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