Username   Password       Forgot your password?  Forgot your username? 

 

New Method of Metal Magnetic Memory  Signal Measuring and Denoising

Volume 15, Number 2, February 2019, pp. 497-504
DOI: 10.23940/ijpe.19.02.p14.497504

S. J. Deng, H. L. Chen, L. W. Tang, and W. Wang

Department of Artillery Engineering, Army Engineering University, Shijiazhuang, 050003, China

(Submitted on October 15, 2018; Revised on November 17, 2018; Accepted on December 12, 2018)

Abstract:

The metal magnetic memory (MMM) technique can be effective in determining the initial damage of materials and structures in service and is partly applied in engineering. However, the real signals measured in engineering practice usually contain interference of the background magnetic field and measurement noise. For the influence of the background magnetic field, we designed a measuring probe constituted by two magnetic sensors that were arranged at different heights in the same vertical direction. Through the channel compensated method, we extracted principal features of the self-magnetic leakage field (SMLF) signal. As for the influence of measurement noise, we built the structure elements combined with the SMLF signal characteristic and investigated multi-scale morphological filtering to reduce the noise. The simulation and experiment results show that the proposed methods can not only suppress the background magnetic field and many kinds of noise but also protect the SMLF signal detail effectively.

 

References: 15

        1. P. Shi, P. Zhang, and K. Jin, “Thermo-Magneto-Elastoplastic Coupling Model of Metal Magnetic Memory Testing Method for Ferromagnetic Materials,” Journal of Applied Physics, Vol. 123, No. 14, pp. 145102, 2018
        2. H. L. Chen, C. L. Wang, and X. Z. Zuo, “Research on Methods of Defect Classification based on Metal Magnetic Memory,” Ndt & E International, Vol. 99, No. 1, pp. 82-87, 2017
        3. A. Dubov, “A Study of Metal Properties using the Method of Magnetic Memory,” Metal Science & Heat Treatment, Vol. 39, No. 9, pp. 401-405, 1997
        4. A. Dubov and S. Kolokolnikov, “The Metal Magnetic Memory Method Application for Online Monitoring of Damage Development in Steel Pipes and Welded Joints Specimens,” Welding in the World, Vol. 57, No. 1, pp. 123-136, 2013
        5. S. Bao, M. Fu, and Y. Gu, “Quantitative Characterization of Stress Concentration of Low-Carbon Steel by Metal Magnetic Memory Testing,” Materials Evaluation, Vol. 75, No. 3, pp. 397-405, 2017
        6. B. Liu, Y. Y. He, and H. Zhang, “Study on Characteristics of Magnetic Memory Testing Signal based on the Stress Concentration Field,” Iet Science Measurement & Technology, Vol. 11, No. 1, pp. 2-8, 2017
        7. W. S. Singh, R. Stegemann, and M. Kreutzbruck, “Mapping of Deformation-Induced Magnetic Fields in Carbon Steels using a GMR Sensor based Metal Magnetic Memory Technique,” Journal of Nondestructive Evaluation, Vol. 37, No. 2, pp. 21-27, 2018
        8. M. B. Arkulis, M. P. Baryshnikov, and N. I. Mishenva, “On Problems of Applicability of the Metal Magnetic-Memory Method in Testing the Stressed-Deformed State of Metallic Constructions,” Russian Journal of Nondestructive Testing, Vol. 45, No. 8, pp. 526-528, 2009
        9. Y. Y. Reutov, “A Ferromagnetic Disk in a Constant Axially Symmetric Inhomogeneous Magnetic Field,” Russian Journal of Nondestructive Testing, Vol. 51, No. 3, pp. 146-150, 2015
        10. H. P. Wang, L. H. Dong, S. Y. Dong, and B. S. Xu, “Fatigue Damage Evaluation by Metal Magnetic Memory Testing,” Journal of Central South University, Vol. 21, No. 1, pp. 65-70, DOI 10.1007/s11771-014-1916-5, 2014
        11. M. X. Xu, Z. H. Chen, and M. Q. Xu, “Micro-Mechanism of Metal Magnetic Memory Signal Variation During Fatigue,” International Journal of Minerals, Metallurgy and Materials, Vol. 21, No. 3, pp. 259-265, 2014
        12. K. Yao, Z. D. Wang, and B. Deng, “Experimental Research on Metal Magnetic Memory Method,” Experimental Mechanics, Vol. 52, No. 3, pp. 305-314, 2012
        13. J. Chou, R. C. Weger, and J. M. Ligtenberg, “Segmentation of Polar Scenes using Multi-Spectral Texture Measures and Morphological Filtering,” International Journal of Remote Sensing, Vol. 15, No. 5, pp. 1019-1036, 1994
        14. Y. Li, X. Liang, and M. J. Zuo, “A New Strategy of Using a Time-Varying Structure Element for Mathematical Morphological Filtering,” Measurement, Vol. 106, pp. 53-65, 2017
        15. Y. Li, X. Liang, and J. Lin, “Train Axle Bearing Fault Detection using a Feature Selection Scheme based Multi-Scale Morphological Filter,” Mechanical Systems & Signal Processing, Vol. 101, pp. 435-448, 2018

         

        Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

         
        This site uses encryption for transmitting your passwords. ratmilwebsolutions.com