Improving the effectiveness of the DiagBelt+ diagnostic system - analysis of the impact of measurement parameters on the quality of signals

• Autorzy: Błażej Ryszard , Jurdziak Leszek , Kirjanów-Błażej Agata , Rzeszowska Aleksandra , Paweł Kostrzewa

Identyfikatory

DOI

10.17531/ein/187275

• Języki publikacji: EN

Abstrakty

EN

The key issue for ensuring economic efficiency and continuous operation of conveyor transport is the recognition of the condition of the belt core. Faults in steel cords in the core are not visible during routine visual inspections, but they can be identified using magnetic diagnostic systems such as DiagBelt+. The article presents an analysis of the impact of the sensitivity threshold of the DiagBelt+ system, the diameter of cords in the core, and the belt speed on the quality of signals representing known damage: cutting of cords, their absence, and a reduction in the cross-section of the cord. The study focuses on defects to cords across the belt, as they can weaken the belt's strength and lead to a complete belt failure. The proposed results and analyses contribute to the improvement of the methodology for magnetic examination of the core's condition and the developed diagnostic system DiagBelt+. Consequently, this enhances the reliability and safety of belt conveyors in various industries, including brown coal mines where it has been implemented (PGE GiEK SA KWB O/Bełchatów), as well as in hard coal, limestone, and copper ore mines where it is used to assess the condition of belts with steel cords.

Słowa kluczowe

EN

NDT testing; conveyor belt; DiagBelt system; magnetic method; accuracy

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 3
• Strony: art. no. 187275
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Błażej Ryszard

• Wroclaw University of Science and Technology, Poland

• https://orcid.org/0000-0002-8802-9865

autor

Jurdziak Leszek

• Wroclaw University of Science and Technology, Poland

• https://orcid.org/0000-0003-2353-8420

autor

Kirjanów-Błażej Agata

• Wroclaw University of Science and Technology, Poland

• https://orcid.org/0000-0002-0011-6180

autor

Rzeszowska Aleksandra

• Wroclaw University of Science and Technology, Poland

• https://orcid.org/0000-0002-6266-2806

autor

Paweł Kostrzewa

• BESTGUM Polska, Poland

Bibliografia

• 1. Andrejiova, M. et al.: Measurement and simulation of impact wear damage to industrial conveyor belts. Wear. 368–369, (2016). https://doi.org/10.1016/j.wear.2016.10.010.
• 2. Bajda, M., Hardygóra, M.: Laboratory tests of operational durability and energy-efficiency of conveyor belts. In: IOP Conference Series: Earth and Environmental Science. (2019). https://doi.org/10.1088/1755-1315/261/1/012002.
• 3. BeltScan Pty: https://www.beltscan.com/products/belt-scanner-portable-condition-monitoring.html.
• 4. Błażej, R. et al.: Profitability of conveyor belt refurbishment and diagnostics in the light of the circular economy and the full and effective use of resources. Energies (Basel). 15, 20, (2022). https://doi.org/10.3390/en15207632.
• 5. Błażej, R. et al.: The use of magnetic sensors in monitoring the condition of the core in steel cord conveyor belts – Tests of the measuring probe and the design of the DiagBelt system. Measurement (Lond). 123, (2018). https://doi.org/10.1016/j.measurement.2018.03.051.
• 6. Bortnowski, P. et al.: Types and causes of damage to the conveyor belt – Review, classification and mutual relations, (2022). https://doi.org/10.1016/j.engfailanal.2022.106520.
• 7. Bugaric, U. et al.: Lost production costs of the overburden excavation system caused by rubber belt failure. Eksploatacja i Niezawodnosc. 14, 4, (2012).
• 8. Che, H. et al.: A reliability model for load-sharing K-out-of-N systems subject to soft and hard failures with dependent workload and shock effects. Eksploatacja i Niezawodnosc. 22, 2, (2020). https://doi.org/10.17531/ein.2020.2.8.
• 9. Chen, X. et al.: Wear and aging behavior of vulcanized natural rubber nanocomposites under high-speed and high-load sliding wear conditions. Wear. 498–499, (2022). https://doi.org/10.1016/j.wear.2022.204341.
• 10. Deng, Y., Li, A.: Measurement-Based Damage Detection for Expansion Joints. In: Structural Health Monitoring for Suspension Bridges. (2019). https://doi.org/10.1007/978-981-13-3347-7_3.
• 11. Doroszuk, B., Król, R.: Analysis of conveyor belt wear caused by material acceleration in transfer stations. Mining Science. 26, (2019). https://doi.org/10.5277/msc192615.
• 12. Doroszuk, B., Król, R.: Conveyor belt wear caused by material acceleration in transfer stations. Mining Scince. 26, (2020). https://doi.org/10.37190/msc192615.
• 13. Fedorko, G. et al.: Failure analysis of textile rubber conveyor belt damaged by dynamic wear. Eng Fail Anal. 28, (2013). https://doi.org/10.1016/j.engfailanal.2012.10.014.
• 14. Fedorko, G.: Implementation of Industry 4.0 in the belt conveyor transport. MATEC Web of Conferences. 263, (2019). https://doi.org/10.1051/matecconf/201926301001.
• 15. Fedorko, G. et al.: Possibilities of failure analysis for steel cord conveyor belts using knowledge obtained from non-destructive testing of steel ropes. Eng Fail Anal. 67, (2016). https://doi.org/10.1016/j.engfailanal.2016.05.026.
• 16. Fourie, J. et al.: Condition Monitoring of Fabric-Reinforced Conveyor Belting Using Digital X-Ray Imaging. Bulk Solids Handling. 25, 290–294 (2015).
• 17. Guo, X. et al.: Belt tear detection for coal mining conveyors. Micromachines (Basel). 13, 3, (2022). https://doi.org/10.3390/mi13030449.
• 18. Guo, X. et al.: Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network. Sensors. 22, 9, (2022). https://doi.org/10.3390/s22093485.
• 19. Guo, X. et al.: Machine vision based damage detection for conveyor belt safety using Fusion knowledge distillation. Alexandria Engineering Journal. 71, 161–172 (2023). https://doi.org/10.1016/j.aej.2023.03.034.
• 20. Hua, J. et al.: High-resolution damage detection based on local signal difference coefficient model. Struct Health Monit. 14, 1, (2015). https://doi.org/10.1177/1475921714546060.
• 21. Ilic, D.: Development of design criteria for reducing wear in iron ore transfer chutes. Wear. 434–435, (2019). https://doi.org/10.1016/j.wear.2019.202986.
• 22. Jurdziak, L. et al.: Comparison of different metrics of belt condition used in lignite mines for taking decision about belt segments replacement and refurbishment. Presented at the (2024). https://doi.org/10.1007/978-3-031-44282-7_39.
• 23. Komander, H. et al.: Assessment methods of conveyor belts impact resistance to the dynamic action of a concentrated load. Eksploatacja i Niezawodnosc. 16, 4, (2014).
• 24. Lei, Y. et al.: Structural damage detection with limited input and output measurement signals. Mech Syst Signal Process. 28, (2012). https://doi.org/10.1016/j.ymssp.2011.07.026.
• 25. Li, X.-G. et al.: Automatic Defect Detection Method for the Steel Cord Conveyor Belt Based on Its X-Ray Images. In: 2011 International Conference on Control, Automation and Systems Engineering (CASE). pp. 1–4 IEEE (2011). https://doi.org/10.1109/ICCASE.2011.5997624.
• 26. Ma, N. et al.: Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory. Energies (Basel). 16, 14, 5240 (2023). https://doi.org/10.3390/en16145240.
• 27. Mazurek, P. et al.: Influence of the Size of Damage to the Steel Wire Rope on the Magnetic Signature. Sensors. 22, 21, 8162 (2022). https://doi.org/10.3390/s22218162.
• 28. Mendes, D. et al.: Enhanced real-time maintenance management model—a step toward Industry 4.0 through Lean: Conveyor belt operation case study. Electronics (Basel). 12, 18, (2023). https://doi.org/10.3390/electronics12183872.
• 29. Mendler, A. et al.: Sensor placement with optimal damage detectability for statistical damage detection. Mech Syst Signal Process. 170, (2022). https://doi.org/10.1016/j.ymssp.2021.108767.
• 30. Mi, J. et al.: Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network. Eksploatacja i Niezawodnosc. 22, 1, (2020). https://doi.org/10.17531/ein.2020.1.13.
• 31. Olchówka, D. et al.: Statistical analysis and neural network in detecting steel cord failures in conveyor belts. Energies (Basel). 14, 11, (2021). https://doi.org/10.3390/en14113081.
• 32. Pang, Y.: Intelligent belt conveyor monitoring and control. (2005).
• 33. Radzieński, M. et al.: Improvement of damage detection methods based on experimental modal parameters. Mech Syst Signal Process. 25, 6, (2011). https://doi.org/10.1016/j.ymssp.2011.01.007.
• 34. Roskosz, M. et al.: Self Magnetic Flux Leakage as a Diagnostic Signal in the Assessment of Active Stress - Analysis of Influence Factors. Acta Phys Pol A. 137, 5, 690–692 (2020). https://doi.org/10.12693/APhysPolA.137.690.
• 35. Roskosz, M. et al.: Use of Different Types of Magnetic Field Sensors in Diagnosing the State of Ferromagnetic Elements Based on Residual Magnetic Field Measurements. Sensors. 23, 14, 6365 (2023). https://doi.org/10.3390/s23146365.
• 36. Semrád, K., Draganová, K.: Non-destructive testing of pipe conveyor belts using glass-coated magnetic microwires. Sustainability (Switzerland). 14, 14, (2022). https://doi.org/10.3390/su14148536.
• 37. Walker, P. et al.: Analysis of ore flow through longitudinal belt conveyor transfer point. Eksploatacja i Niezawodnosc. 22, 3, (2020). https://doi.org/10.17531/ein.2020.3.17.
• 38. Wang, J. et al.: Belt vision localization algorithm based on machine vision and belt conveyor deviation detection. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). pp. 269–273 IEEE (2019). https://doi.org/10.1109/YAC.2019.8787667.
• 39. Wang, J., Yang, Q.S.: Sensor selection approach for damage identification based on response sensitivity. Structural Monitoring and Maintenance. 4, 1, (2017). https://doi.org/10.12989/smm.2017.4.1.053.
• 40. Webb, C. et al.: Developing and evaluating predictive conveyor belt wear models. Data-Centric Engineering. 1, 1–2, (2020). https://doi.org/10.1017/dce.2020.1.
• 41. Witoś, M. et al.: NDE of Mining Ropes and Conveyors Using Magnetic Methods. In: International Symposium on Structural Health Monitoring and Nondestructive Testing 4-5 Oct 2018, Saarbrücken – Germany (SHM-NDT 2018) | Vol. 23(12). , Saarbrücken – German (2018).
• 42. Xie, L. et al.: Wear process during granular flow transportation in conveyor transfer. Powder Technol. 288, (2016). https://doi.org/10.1016/j.powtec.2015.10.043.
• 43. Yang, R. et al.: Infrared spectrum analysis method for detection and early warning of longitudinal tear of mine conveyor belt. Measurement (Lond). 165, (2020). https://doi.org/10.1016/j.measurement.2020.107856.
• 44. Yu, J., Zhang, H.: A suspended FBG damage detection sensor based on magnetic drive. Measurement (Lond). 189, (2022). https://doi.org/10.1016/j.measurement.2021.110499.
• 45. Ziehl, P., ElBatanouny, M.: Acoustic emission monitoring for corrosion damage detection and classification. In: Corrosion of Steel in Concrete Structures. (2023). https://doi.org/10.1016/B978-0-12-821840-2.00009-2.