journal article·2024-06

Research on Identification Method of Cable Cross-Sectional Loss Rates Based on Multiple Magnetic Characteristic Indicators

Li Jiang, Hong Zhang, Runchuan Xia, Jianting Zhou, Shuwen Liu, Yaxi Ding

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OpenAlex topics: Non-Destructive Testing Techniques · Magnetic Properties and Applications · Ultrasonics and Acoustic Wave Propagation

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References (35)

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1. Zhang, L., Qiu, G., Chen, Z.: Structural health monitoring methods of cables in cable-stayed bridge: a review. Measurement 168, 108343 (2021). https://doi.org/10.1016/j.measurement.2020.10834310.1016/j.measurement.2020.108343 ↗
2. Zhao, Y., Su, B., Fan, X., Yuan, Y., Zhu, Y.: Corrosion fatigue degradation characteristics of galvanized and galfan high-strength steel wire. Materials 16, 708 (2023). https://doi.org/10.3390/ma1602070810.3390/ma16020708 ↗
3. Hadizadeh-Bazaz, M., Navarro, I.J., Yepes, V.: Life cycle assessment of a coastal concrete bridge aided by non-destructive damage detection methods. J. Mar. Sci. Eng. 11, 1656 (2023). https://doi.org/10.3390/jmse1109165610.3390/jmse11091656 ↗
4. Hosseini, S.M., Azadi, M., Ghasemi-Ghalebahman, A., Jafari, S.M.: Fatigue crack initiation detection in ductile cast iron crankshaft under rotating bending fatigue test using the acoustic emission entropy method. Eng. Fail. Anal. 144, 106981 (2023). https://doi.org/10.1016/j.engfailanal.2022.10698110.1016/j.engfailanal.2022.106981 ↗
5. Li, D., Ou, J., Lan, C., Li, H.: Monitoring and failure analysis of corroded bridge cables under fatigue loading using acoustic emission sensors. Sensors. 12, 3901–3915 (2012). https://doi.org/10.3390/s12040390110.3390/s120403901 ↗
6. Yang, D.-H., Gu, H.-L., Yi, T.-H., Li, H.-N.: Bridge cable anomaly detection based on local variability in feature vector of monitoring group cable forces. J. Bridge Eng. 28, 04023030 (2023). https://doi.org/10.1061/JBENF2.BEENG-608410.1061/JBENF2.BEENG-6084 ↗
7. Nguyen, M.-H., Truong, T.-D.-N., Le, T.-C., Ho, D.-D.: Identification of tension force in cable structures using vibration-based and impedance-based methods in parallel. Buildings 13, 2079 (2023). https://doi.org/10.3390/buildings1308207910.3390/buildings13082079 ↗
8. Fei, Y., Royer, R.L., Rose, J.L.: Ultrasonic guided wave imaging techniques in structural health monitoring. J. Intell. Mater. Syst. Struct. 21, 377–384 (2010). https://doi.org/10.1177/1045389X0935602610.1177/1045389X09356026 ↗
9. Zang, X., Xu, Z.-D., Lu, H., Zhu, C., Zhang, Z.: Ultrasonic guided wave techniques and applications in pipeline defect detection: a review. Int. J. Press. Vessels Pip. 206, 105033 (2023). https://doi.org/10.1016/j.ijpvp.2023.10503310.1016/j.ijpvp.2023.105033 ↗
10. Cui, H., Ke, D., Gao, Q., Li, S.: Mesoscopic pore characteristics analysis of aged bridge concrete based on X-ray computed tomography. J. Build. Eng. 78, 107739 (2023). https://doi.org/10.1016/j.jobe.2023.10773910.1016/j.jobe.2023.107739 ↗
11. Suresh, V., Abudhahir, A., Daniel, J.: Characterization of defects on ferromagnetic tubes using magnetic flux leakage. IEEE Trans. Magn. 55, 6200510 (2019). https://doi.org/10.1109/TMAG.2019.290165910.1109/TMAG.2019.2901659 ↗
12. Kandroodi, M.R., Araabi, B.N., Bassiri, M.M., Ahmadabadi, M.N.: Estimation of depth and length of defects from magnetic flux leakage measurements: verification with simulations, experiments, and pigging data. IEEE Trans. Magn. 53, 6200310 (2017). https://doi.org/10.1109/TMAG.2016.263152510.1109/TMAG.2016.2631525 ↗
13. Zhang, J., Peng, F., Chen, J.: Quantitative detection of wire rope based on three-dimensional magnetic flux leakage color imaging technology. IEEE Access 8, 104165–104174 (2020). https://doi.org/10.1109/ACCESS.2020.299958410.1109/ACCESS.2020.2999584 ↗
14. Lee, J., Wang, D., Dharmawan, I.D.M.O.: Theoretical model of self-magnetic flux leakage and its application in estimating the depth direction of a fatigue crack. Appl. Sci. 13, 533 (2022). https://doi.org/10.3390/app1301053310.3390/app13010533 ↗
15. Dubov, A.A.: A study of metal properties using the method of magnetic memory. Met. Sci. Heat Treat. 39, 401–405 (1997). https://doi.org/10.1007/BF0246906510.1007/BF02469065 ↗
16. Bao, S., Jin, P., Zhao, Z., Fu, M.: A review of the metal magnetic memory method. J. Nondestruct. Eval. 39, 11 (2020). https://doi.org/10.1007/s10921-020-0652-z10.1007/s10921-020-0652-z ↗
17. Tong, K., Zhang, H., Zhao, R., Zhou, J., Ying, H.: Investigation of SMFL monitoring technique for evaluating the load-bearing capacity of RC bridges. Eng. Struct. 293, 116667 (2023). https://doi.org/10.1016/j.engstruct.2023.11666710.1016/j.engstruct.2023.116667 ↗
18. Xu, F., Wang, X.: Inspection method of cable-stayed bridge using magnetic flux leakage detection: principle, sensor design, and signal processing. J. Mech. Sci. Technol. (2012). https://doi.org/10.1007/s12206-011-1234-x10.1007/s12206-011-1234-x ↗
19. Xia, R., Zhou, J., Zhang, H., Zhou, D., Zhang, Z.: Experimental study on corrosion of unstressed steel strand based on metal magnetic memory. KSCE J. Civ. Eng. 23, 1320–1329 (2019). https://doi.org/10.1007/s12205-019-0715-910.1007/s12205-019-0715-9 ↗
20. Snarskii, A.A., Zhenirovskyy, M., Meinert, D., Schulte, M.: An integral equation model for the magnetic flux leakage method. NDT & E Int. 43, 343–347 (2010). https://doi.org/10.1016/j.ndteint.2010.01.00810.1016/j.ndteint.2010.01.008 ↗
21. Xia, R., Zhou, J., Zhang, H., Liao, L., Zhao, R., Zhang, Z.: Quantitative study on corrosion of steel strands based on self-magnetic flux leakage. Sensors 18, 1396 (2018). https://doi.org/10.3390/s1805139610.3390/s18051396 ↗
22. Qu, Y., Zhang, H., Zhao, R., Liao, L., Zhou, Y.: Research on the method of predicting corrosion width of cables based on the spontaneous magnetic flux leakage. Materials 12, 2154 (2019). https://doi.org/10.3390/ma1213215410.3390/ma12132154 ↗
23. Meng, Q., Pan, P., Yang, X., Song, J., Wang, J.: Self-magnetic flux leakage-based detection and quantification for high-strength steel wires of bridge cables considering corrosion-fatigue coupling effect. J. Magn. Magn. Mater. 561, 169641 (2022). https://doi.org/10.1016/j.jmmm.2022.16964110.1016/j.jmmm.2022.169641 ↗
24. Ma, X., Su, S., Wang, W., Yang, Y., Yi, S., Zhao, X.: Damage location and numerical simulation for steel wire under torsion based on magnetic memory method. JAE 60, 223–246 (2019). https://doi.org/10.3233/JAE-18007510.3233/JAE-180075 ↗
25. Zhang, H., Wu, X., Li, H., Jiang, L., Qiu, J., Zhou, J.: Research on self-leakage flux magnetic identification method of the most dangerous section of parallel wire cable under multi-point broken wire condition. Measurement (2024). https://doi.org/10.1016/j.measurement.2024.11418510.1016/j.measurement.2024.114185 ↗
26. Zhang, H., Liao, L., Zhao, R., Zhou, J., Yang, M., Zhao, Y.: A new judging criterion for corrosion testing of reinforced concrete based on self-magnetic flux leakage. Int. J. Appl. Electromagnet Mech 54, 123–130 (2017). https://doi.org/10.3233/JAE-16012210.3233/JAE-160122 ↗
27. Xia, R.: Probability evaluation method of cable corrosion degree based on self-magnetic flux leakage. J. Magn. Magn. Mater. (2021). https://doi.org/10.1016/j.jmmm.2020.16754410.1016/j.jmmm.2020.167544 ↗
28. Xia, R., Zhang, H., Zhou, J., Liao, L., Yang, W., Li, Y.: Corrosion non-destructive testing of loaded steel strand based on self-magnetic flux leakage effect. Nondestr. Test. Eval. 37, 56–70 (2022). https://doi.org/10.1080/10589759.2021.190084610.1080/10589759.2021.1900846 ↗
29. Zhang, S.: Defect-depth-field algorithm for simulating magnetic flux leakage signals based on discrete magnetic dipole model. NDT & E Int. 139, 102939 (2023). https://doi.org/10.1016/j.ndteint.2023.10293910.1016/j.ndteint.2023.102939 ↗
30. Yang, L.J.: The quantitative interpretation by measurement using the magnetic memory method (MMM)-based on density functional theory. NDT & E Int (2013). https://doi.org/10.1016/j.ndteint.2013.01.00210.1016/j.ndteint.2013.01.002 ↗
31. Xu, K., Qiu, X., Tian, X.: Theoretical investigation of metal magnetic memory testing technique for detection of magnetic flux leakage signals from buried defect. Nondestr. Test. Eval. 33, 45–55 (2018). https://doi.org/10.1080/10589759.2017.129305010.1080/10589759.2017.1293050 ↗
32. Wang, Z.D., Yao, K., Deng, B., Ding, K.Q.: Theoretical studies of metal magnetic memory technique on magnetic flux leakage signals. NDT & E Int. (2010). https://doi.org/10.1016/j.ndteint.2009.12.00610.1016/j.ndteint.2009.12.006 ↗
33. Jia, S.: A self-powered synchronous magnetic flux extraction interface for electromagnetic energy harvesting. Microelectron. J. (2023). https://doi.org/10.1016/j.mejo.2023.10594310.1016/j.mejo.2023.105943 ↗
34. Qu, Y., Zhou, J., Liu, R., Liao, L., Zhao, Q.: Research on the detection of the broken wire damage of a cable in the circumferential directions based on self-magnetic flux leakage. KSCE J. Civ. Eng. 25, 879–890 (2021). https://doi.org/10.1007/s12205-021-0334-010.1007/s12205-021-0334-0 ↗
35. Zhang, H., Li, H.: A multi-dimensional evaluation of wire breakage in bridge cable based on self-magnetic flux leakage signals. J. Magn. Magn. Mater. 566, 170321 (2023). https://doi.org/10.1016/j.jmmm.2022.17032110.1016/j.jmmm.2022.170321 ↗