Nondestructive Testing of Steel Wire Rope Based on Gagnetic Signal and Infrared Information
Source abstract
Abstract
Abstract This paper designs a two-dimensional magnetic signal detection device under weak magnetic excitation, which solves the problem of large volume and single signal acquisition of traditional one-dimensional detection devices. To reduce the original noise, a noise reduction algorithm combining wavelet transform and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed. A super-resolution fusion algorithm is proposed to fuse two-dimensional magnetic signals to achieve image enhancement. Finally, the convolutional neural network is used to extract the features of the two types of images, and then the features are fused, and the support vector machine (SVM) is used to classify. Under the condition of zero broken wire error, compared with the subjectively extracted color features and texture features of the two types of images as the SVM input, this algorithm’s recognition rate is increased by 37.26%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Welding Techniques and Residual Stresses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (17)
Show source-provided references
1. Zhou, P., Zhou, G., Zhu, Z., et al., Health monitoring for balancing tail ropes of a hoisting system using a convolutional neural network, Appl. Sci., 2018, vol. 8(8), p. 1346.10.3390/app8081346 ↗
2. Gao, W., Zhang, D., and Zhu, X., Tension monitoring and defect detection by magnetostrictive longitudinal guided wave for fine wire rope, IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–7.
3. Jia, Y., Liang, K., Wang, P., et al., Enhancement method of magnetic flux leakage signals for rail track surface defect detection, IET Sci. Meas. & Technol., 2020, vol. 14, no. 6, pp. 711–717.10.1049/iet-smt.2018.5651 ↗
4. AbdAlla, A.N., Faraj, M.A., Samsuri, F., et al., Challenges in improving the performance of eddy current testing, Meas. Control, 2019, vol. 52, nos. 1–2, pp. 46–64.10.1177/0020294018801382 ↗
5. Shi, P., Su, S., and Chen, Z., Overview of researches on the nondestructive testing method of metal magnetic memory: Status and challenges, J. Nondestr. Eval., 2020, vol. 39, pp. 1–37.10.1007/s10921-020-00688-z ↗
6. Shi, P., Jin, K., and Zheng, X., A magneto-mechanical model for the magnetic memory method, Int. J. Mech. Sci., 2017, nos. 124–125, pp. 229–241.10.1016/j.ijmecsci.2017.03.001 ↗
7. Ni, C., Hua, L., and Wang, X., Crack propagation analysis and fatigue life prediction for structural alloy steel based on metal magnetic memory testing, J. Magn. Magn. Mater., 2018, vol. 462, no. 9, pp. 144–152.10.1016/j.jmmm.2018.05.019 ↗
8. Liu, S. and Chen, M., Wire rope defect recognition method based on MFL signal analysis and 1D-CNNs, Sensors, 2023, vol. 23, no. 7, p. 3366.10.3390/s23073366 ↗
9. Liu, S., Sun, Y., Ma, W., et al., A new signal processing method based on notch filtering and wavelet denoising in wire rope inspection, J. Nondestr. Eval., 2019, vol. 38, pp. 1–14.10.1007/s10921-019-0580-y ↗
10. Liu, S., Sun, Y., Jiang, X., et al., A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection, Mech. Syst. Signal Process., 2022, vol. 163, p. 108156.10.1016/j.ymssp.2021.108156 ↗
11. Pan, F., Liu, Z., Ren, L., et al., Adaptive local flaw detection based on magnetic flux leakage images with a noise distortion effect for steel wire ropes, IEEE Trans. Ind. Electron., 2023.10.1109/TIE.2023.3273250 ↗
12. Zhang Peng, Research on parameter design of gathering and transportation pipeline in N area, Petrochem. Technol., 2018, vol. 25, no. 7, p. 2.
13. Zhang, J., Li, B., Zhang, Z., et al., Application of a convolutional neural network in wire rope magnetic memory testing, Insight Nondestr. Test. Cond. Monit., 2022, vol. 64, no. 10, pp. 566–572.10.1784/insi.2022.64.10.566 ↗
14. Ye Hui, Research on defect detection system of mine steel wire rope based on three-axis magnetic memory, J. Taiyuan Univ. Technol., 2018.
15. Heideklang, R. and Shokouhi, P., Multi-sensor image fusion at signal level for improved near-surface crack detection, NDT & E Int., 2015, vol. 71, pp. 16–22.10.1016/j.ndteint.2014.12.008 ↗
16. Li, X., Zhang, J., and Shi, J., Quantitative nondestructive testing of broken wires for wire rope based on magnetic and infrared information, J. Sens., 2020, vol. 2020, pp. 1–14.
17. Dubov, A.A., Development of a metal magnetic memory method, Chem. Petrol. Eng., 2012, vol. 47, nos. 11–12, pp. 837–839.10.1007/s10556-012-9559-6 ↗