Author(s)

QingXiang Zhang^*, Qun Zhang, KeLong Xu

Affiliation(s)

Jinan Wantian Machinery Equipment Co., LTD, Jinan 250102, Shandong, China.

Corresponding Author

QingXiang Zhang

ABSTRACT

This paper focuses on the damage detection system for wire ropes under the excitation of annular permanent magnets, and on this basis, conducts structural optimization and simulation research of the wire rope damage detection system. To enhance the strength of the detection signal and extend the service life of the detection unit, the positioning of the detection unit is arranged in such a way that it is not damaged by vibration or proximity, thereby ensuring the stability of the detection effect. Based on Comsol, the role of this method in excitation characteristics and magnetic flux leakage detection is studied. Furthermore, the optimized equipment is tested using numerical simulation methods to ensure that it not only meets the excitation characteristics for defects but also guarantees the extraction of magnetic flux leakage signals from the defective areas, achieving higher reliability. The research findings of this study can provide a theoretical basis for the optimal design of damage monitoring equipment for wire ropes in lifting machinery and have significant guiding importance for improving the overall performance of lifting machinery and equipment.

KEYWORDS

Tower crane; Communication; Magnetic Flux Leakage Detection (MFLD); Wire rope defect detection

CITE THIS PAPER

QingXiang Zhang, Qun Zhang, KeLong Xu. Tower crane wire rope defect detection technology based on Magnetic Flux Leakage Detection(MFLD). World Journal of Management Science. 2024, 2(4): 51-56. DOI: https://doi.org/10.61784/wms3056.

REFERENCES

[1] Liu S, Sun Y, Jiang X, et al. A Review of Wire Rope Detection Methods, Sensors and Signal Processing Techniques. J Nondestruct Eval, 2020, 39, 85. DOI: https://doi.org/10.1007/s10921-020-00732-y.

[2] Dong H J, Jiang X M, Kang K, et al. Detection of External Defects in Steel Wire Rope Based on Image Processing. Advanced Materials Research, 2013, 734-737, 2949-2952. DOI: 10.4028/www.scientific.net/AMR.734-737.2949.

[3] Zhang G Y, Tang Z H, Zhang J, et al. Convolutional autoencoder-based flaw detection for steel wire ropes. Sensors, 2020, 20(22): 6612. DOI: 10.3390/s20226612.

[4] Chen Z, Liu Y M. Broken steel wire rope inspection based on RBF neural network. Advanced Materials Research, 2013, 614-615: 1734-1737. DOI: 10.4028/www.scientific.net/AMR.614-615.1734.

[5] Tse P W, Rostami J. Advanced signal processing methods applied to guided waves for wire rope defect detection. AIP Conference Proceedings, 2016, 1706: 030006. DOI: 10.1063/1.4940478.

[6] Mazurek P, Roskosz M, Kwasniewski J. Novel diagnostic of steel wire rope with passive magnetic methods. IEEE Magnetics Letters, 2022, 13: 2500705. DOI: 10.1109/LMAG.2021.3128828.

[7] Xia H, Yan R, Wu J B, et al. Visualization and quantification of broken wires in steel wire ropes based on induction thermography. IEEE Sensors Journal, 2021, 21(17): 18497-18503. DOI: 10.1109/JSEN.2021.3088158.

[8] Zhang Y, Jing L, Xu W, et al. A sensor for broken wire detection of steel wire ropes based on the magnetic concentrating principle. Sensors, 2019, 19: 3763. DOI: 10.3390/s19173763.