Journals →  Chernye Metally →  2023 →  #3 →  Back

Technological Measurements and Defect Control
ArticleName Recognition of defects in hoisting ropes of metallurgical equipment by an optical method using neural networks
DOI 10.17580/chm.2023.03.13
ArticleAuthor A. A. Kulchitskiy, O. K. Mansurova, M. Yu. Nikolaev

St. Petersburg Mining University, St. Petersburg, Russia:

A. A. Kulchitskiy, Dr. Eng., Head of the Dept. of Automation of Technological Processes and Production, e-mail:
O. K. Mansurova, Cand. Eng., Associate Prof., Dept. of Automation of Technological Processes and Production, e-mail:
M. Yu. Nikolaev, Undergraduate Student, Dept. of Automation of Technological Processes and Production, e-mail:


The article considers the problem of controlling the condition of hoisting ropes. The existing methods and control systems for hoisting ropes have been analyzed, according to the results of which it was found that the magnetic control systems can detect defects and damage only after they have reached the stage of significant damage. The use of optical inspection methods and pattern recognition technology using convolutional pyramidal neural networks to detect defects in hoisting ropes of metallurgical equipment was considered. It describes the layout solution of the control system of hoisting ropes using the optical method with the angle mirror converter, which allows controlling the entire surface of the hoisting rope using a single digital camera, which will reduce the cost of the hardware. Analysis of research into methods of processing visual information in order to detect defects of control objects showed the prospects of using convolutional neural networks. During the study, neural networks were trained on images of ropes with different characteristics based on the software library TensorFlow. The results of assessing the reliability of detecting defects and damage to hoisting ropes noted the influence of lighting and the distance at which the object under test is located on the reliability of recognition of rope defects and faults. An image preprocessing algorithm with removal of the background component, filtering and scaling of the image has been proposed to improve the reliability of hoisting rope defect and damage detection. A qualitative assessment of applying neural networks to determine the type of defects has been obtained, and the possibility of detecting them with an extraction coefficient of 0.80–0.89 using the proposed algorithm has been shown.

keywords Damage detection, steel wire ropes, technical vision, machine learning, optical systems, monitoring, neural networks

1. Ojeda Pardo F. R., Sánchez Figueredo R. P., Belette Fuentes O., Quiroz Cabascango V. E., Mosquera Urbano A. P. Metallographic properties evaluation of the specimens obtained by the vibratory method (cast iron ISO 400-12). Journal of Physics: Conference Series. 2022. 2388. p. 012058. DOI: 10.1088/1742-6596/2388/1/012058
2. Savchenkov S., Beloglazov I. Features of the process obtaining of Mg-Zn-Y master alloy by the metallothermic recovery method of yttrium fluoride melt. Crystals. 2022. Vol. 12, Iss. 6. p. 771. DOI: 10.3390/CRYST12060771
3. Bolobov V. I., Chupin S. A., Bochkov V. S., Akhmerov E. V., Plaschinskiy V. A. The effect of finely divided martensite of austenitic high manganese steel on the wear resistance of the excavator buckets teeth. Key Engineering Materials. 2020. Vol. 854. pp. 3–9. DOI: 10.4028/
4. Shestakov A. K., Sadykov R. M., Petrov P. A. Multifunctional crust breaker for automatic alumina feeding system of aluminum reduction cell. E3S Web of Conferences. 2021. Vol. 266. DOI: 10.1051/e3sconf/202126609002
5. Fedorova E., Pupysheva E., Morgunov V. Modelling of red-mud particle-solid distribution in the feeder cup of a thickener using the combined CFD-DPM approach. Symmetry. 2022. Vol. 14. p. 2314. DOI: 10.3390/SYM14112314
6. Bazhin V. Y., Nguyen H. H. Vietnamese metallurgy on the way out of the crisis with the use of automated control systems. AIP Conference Proceedings. 2022. 2467. 030018. DOI: 10.1063/5.0092750
7. Cabascango V. E. Q., Bazhin V. Y., Martynov S. A., Pardo F. R. O. Automatic control system for thermal state of reverberatory furnaces in production of nickel alloys. Metallurgist. 2022. Vol. 66. pp. 104–116. DOI: 10.1007/S11015-022-01304-3
8. Bolshunov A. V., Vasilev D. A., Ignatiev S. A., Dmitriev A. N., Vasilev N. I. Mechanical drilling of glaciers with bottom-hole scavenging with compressed air. Ice and Snow. 2022. Vol. 62. pp. 35–46. DOI: 10.31857/S2076673422010114
9. Shklyarskiy Y. E., Batueva D. E. Operation mode selection algorithm development of a wind-diesel power plant supply complex. Journal of Mining Institute. 2022. Vol. 253. pp. 115–126. DOI: 10.31897/PMI.2022.7
10. Bazhin V., Masko O. Monitoring of the behaviour and state of nanoscale particles in a gas cleaning system of an ore‐thermal furnace. Symmetry. 2022. Vol. 14, Iss. 5. p. 923. DOI: 10.3390/SYM14050923
11. GOST 33718–2015. Cranes. Wire ropes. Care and maintenance, inspection and discard. Introduced: 01.04.2017.
12. Zhou P., Zhou G., Zhu Z., He Z., Ding X., Tang C. A review of non-destructive damage detection methods for steel wire ropes. Appl. Sci. 2019. Vol. 9. pp. 2771.

13. RD ROSEK-012–97. Steel ropes. Control and rejection standards. Moscow: ROSEK, 1997. 49 p.
14. Awrejcewicz J., Oikonomou V. K., Boikov A., Payor V. The present issues of control automation for levitation metal melting. Symmetry. 2022. Vol. 14. p. 1968. DOI: 10.3390/SYM14101968
15. Sukhorukov V. V., Kotelnikov V. S. Monitoring of the state of steel ropes by automated means of technical diagnostics. Bezopasnost truda v promyshlennosti. 2019. No. 9. pp. 72–81.
16. Wire rope monitoring gives customers a lifting efficiency, production line safety. Available at: Accessed: 06.01.2023.
17. Wire rope inspection & monitoring system. Available at: Accessed: 06.01.2023.
18. Sergeev V. V., Cheremisina O. V., Fedorov A. T., Gorbacheva A. A., Balandinsky D. A. Interaction features of sodium oleate and oxyethylated phosphoric acid esters with the apatite surface. ACS Omega. 2022. Vol. 7, Iss. 3. pp. 3016–3023. DOI: 10.1021/acsomega.1c06047
19. Cheremisina O. V., Ponomareva M. A., Sergeev V. V., Mashukova Y. A., Balandinsky D. A. Extraction of rare earth metals by solid-phase extractants from phosphoric acid solution. Metals. 2021. Vol. 11. p. 991. DOI: 10.3390/met11060991
20. Greaves D., Jin S., Wong P., Kuskova Y. V., Erokhina O. O., Simakov A. S. Problematics and perspectives of the development of automatic control systems for concentration tables using computer simulation. Journal of Physics: Conference Series. 2019. Vol. 1384, Iss. 1. 012023. DOI: 10.1088/1742-6596/1384/1/012023
21. Simakov A. S., Trifonova M. E., Gorlenkov D. V. Virtual analyzer of the voltage and current spectrum of the electric arc in electric arc furnaces. Russian Metallurgy (Metally). 2021. Vol. 6. pp. 713–719. DOI: 10.1134/S0036029521060252
22. Ameyt Yu. et al. Opportunities to improve visual inspection of ropes. OITAF recommendation. No. 30. 81 p.
23. Kashurin R. R., Gerasev S. A., Litvinova T. E., Zhadovskiy I. T. Prospective recovery of rare earth elements from waste. Journal of Physics: Conference Series. 2020. Vol. 1679. p. 052070. DOI: 10.1088/1742-6596/1679/5/052070
24. Tarabarinova T. A., Golovina E. I. Capitalization of mineral resources as an innovation ecological strategy. Geology and Mineral Resources of Siberia. 2021. Vol. 4. pp. 86–96. DOI: 10.20403/2078-0575-2021-4-86-96
25. Litvinova T., Kashurin R., Zhadovskiy I., Gerasev S. The kinetic aspects of the dissolution of slightly soluble lanthanoid carbonates. Metals. 2021. Vol. 11. p. 1793. DOI: 10.3390/MET11111793
26. Zakirova G., Pshenin V., Tashbulatov R., Rozanova L. Modern bitumen oil mixture models in ashalchinsky field with low-viscosity solvent at various temperatures and solvent concentrations. Energies. 2022. Vol. 16. p. 395. DOI: 10.3390/EN16010395
27. Kulchitskiy А. А., Potapov А. I., Smirnov А. G., Boykov V. I. Geometry control system for axisymmetric products with an angular mirror transducer. Izvestiya vuzov. Priborostroenie. 2020. Vol. 63. No. 8. pp. 720–726.
28. Yaman O., Karakose M. Auto-correlation based elevator rope monitoring and fault detection approach with image processing. Proceedings of the 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey, 16–17 September 2017. pp. 1–5. DOI: 10.1109/IDAP.2017.8090176
29. Vasilyeva N. V., Boikov A. V., Erokhina O. O., Trifonov A. Y. Automated digitization of radial charts. Journal of Mining Institute. 2021. Vol. 247. pp. 82–87. DOI: 10.31897/PMI.2021.1.9
30. Sun H. X., Zhang Y. H., Luo F. L. Texture segmentation and boundary recognition of wire rope images in complicated background. Acta Photonica Sinica. 2010. Vol. 39. pp. 1666–1671.
31. Makhov V. Е., Potapov А. I., Shaldaev S. Е. Investigation of image boundaries by contrast extraction using an optical-electronic system. Part 1. Scientific and methodological principles of image border control by contrast extraction. Kontrol. Diagnostika. 2017. No. 10. pp. 44–51.
32. Kofnov О. V. Model and algorithms for digital image processing for estimating the geometric parameters of materials with a periodic structure: Dissertation … of Candidate of Engineering Sciences. Saint Petersburg: Saint Petersburg State University of Industrial Technologies and Design, 2015. 175 p.
33. Pshenin V., Liagova A., Razin A., Skorobogatov A., Komarovsky M. Robot crawler for surveying pipelines and metal structures of complex spatial configuration. Infrastructures. 2022. Vol. 7. p. 75. DOI: 10.3390/INFRASTRUCTURES7060075
34. Platzer E. S., Nägele J., Wehking K. H., Denzler J. HMM – based defect localization in wire ropes – a new approach to unusual subsequence recognition. Lecture Notes in Computer Science. 2009. Vol. 5748. pp. 442–451. DOI: 10.1007/978-3-642-03798-6_45
35. Platzer E. S., Wehking K. H., Denzler J. On the suitability of different features for anomaly detection in wire ropes. Proceedings of the International Conference on Computer Vision, Imaging and Computer Graphics, Lisboa, Portugal, 5–8 February 2009. pp. 296–308.
36. Boikov A., Payor V., Savelev R., Kolesnikov A. Synthetic. Data generation for steel defect detection and classification using deep learning. Symmetry. 2021. Vol. 13, Iss. 7. 1176. DOI: 10.3390/sym13071176
37. Zakharov L., Martyushev D., Ponomareva I. N. Predicting dynamic formation pressure using artificial intelligence methods. Journal of Mining Institute. 2022. Vol. 253. pp. 23–32. DOI: 10.31897/PMI.2022.11
38. Zhou P., Zhou G., He Z., Tang C., Zhu Z., Li W. A novel texture-based damage detection method for wire ropes. Measurement. 2019. Vol. 148. 106954. DOI: 10.1016/j.measurement.2019.106954
39. Huang X., Liu Z., Zhang X., Kang J. et al. Surface damage detection for steel wire ropes using deep learning and computer vision techniques. Measurement. 2020. Vol. 161. 107843. DOI: 10.1016/j.measurement.2020.107843
40. Bulatov V. V. Optoelectronic system for detecting sheet glass flaws based on vision technology. Dissertation … of Candidate of Engineering Sciences. Saint Petersburg: Saint Petersburg Mining University, 2013. 149 p.

Language of full-text russian
Full content Buy