journal article·2025-09-15Open access

A Method for Assessing the Performance of Breaking Hammers Based on Acoustic Signal and Video Analysis

Jacek Wodecki, Przemysław Dąbek, Pavlo Krot, Adam Wróblewski, Radosław Zimroz

Applied Sciences· Volume 15· Issue 18

Abstract

Mining is a branch of industry that continuously implements new technologies to optimise processes, in terms of increasing performance and decreasing the negative impact on the environment. To enhance the efficiency of the rock material fragmentation process, it is proposed to replace the existing hydraulic hammer with a fully electric alternative. This study used simple data-acquisition methods, including acoustic signals, vibrations, electric currents, and video recordings, to compare the efficiency of both hammers. Measurements were taken for hydraulic and electric hammers under comparable conditions, using the same boom, operator, and rock material. Two methodologies are proposed: the first one is based on acoustic data measurement and parametrisation, and the second one, for validation purposes, is based on visual (video) data analysis. The acoustic approach analyses the stability parameter (α) of the α-stable distribution, the shape parameter (ν) of the t Location–Scale distribution that can assess signal impulsiveness, and three additional basic parameters (number of peaks, sum of amplitudes of impulses, and duration of the cycle for each truck/loader). The visual approach, used for validation, detects individual rock sizes and roughly evaluates the number of oversized pieces. However, it is a manual approach, due to challenging conditions. The results of both methods demonstrate that the electric hammer is more than three times more efficient. This increased efficiency is attributable to adaptive impact frequency control, a feature that is unavailable in hydraulic hammers.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Geotechnical Engineering and Underground Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wrocław, Poland

Funding

European Institute of Innovation and Technology (EIT) · EIT RawMaterials GmbH · Polish Ministry of Education and Science

References (57)

Show source-provided references
1. Przegląd mechanicznych sposobów urabiania skał zwięzłych
2. Overview on vertical and directional drilling technologies for the exploration and exploitation of deep petroleum resources10.1007/s40948-016-0038-y ↗
3. Mining technology with drilling-blasting operations
4. Xu, C., Zhu, Y., Song, D., Guo, X., Liu, X., Wang, E., and Lu, R. (2022). Comparison of DEM Models with Different Elemental Dimensions for TBM Disc Cutter Rock Fragmentation. Sustainability, 14.10.3390/su141912909 ↗
5. Analysis of noise emission for selected crushing devices
6. Determination of dust emission level for various crushing devices
7. Wang, X., Zhang, H., Dong, Z., Li, J., Qin, C., Yang, B., and Zhou, Y. (2024). Characterizing Rock-Breaking Performance of PDC Cutters via Stability Metrics and Energy Consumption in FDEM Simulations. Sci. Rep., 14.10.1038/s41598-024-76293-1 ↗
8. Numerical investigation of rock-breaking mechanisms and influencing factors of different PDC cutters during rotary percussion drilling10.1016/j.geoen.2024.213144 ↗
9. Liu, Y., He, L., Dai, L., Shen, K., and Ba, Q. (2023). Analysis of Rock Breaking Parameters and Simulation of Mechanical Characteristics of Multi-Nozzle Jet Impact. Sustainability, 15.10.3390/su151612414 ↗
10. Rock-breaking characteristics of three-ribbed ridge nonplanar polycrystalline diamond compact cutter and its application in plastic formations10.2118/221492-PA ↗
11. Design optimization of room and pillar mines: A case study of the Xianglushan tungsten mine10.1144/qjegh2017-037 ↗
12. Banasiewicz, A., Moosavi, F., Kotyla, M., Śliwiński, P., Krot, P., Wodecki, J., and Zimroz, R. (2023). Forecasting of NOx Emissions of Diesel LHD Vehicles in Underground Mines—An ANN-Based Regression Approach. Appl. Sci., 13.10.3390/app13179965 ↗
13. Kotyla, M., Banasiewicz, A., Krot, P., Śliwiński, P., and Zimroz, R. (2024). NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods. Electronics, 13.10.3390/electronics13061095 ↗
14. Milanesi, F. (2009). Design Optimization and Control Strategies for PM Multiphase Tubular Linear Actuators. [Ph.D. Thesis, Università di Bologna].
15. Wróblewski, A., Krot, P., Zimroz, R., Mayer, T., and Peltola, J. (2023). Review of Linear Electric Motor Hammers—An Energy-Saving and Eco-Friendly Solution in Industry. Energies, 16.10.3390/en16020959 ↗
16. Studies of The Durability of Belt Conveyor Idlers with Working Loads Taken into Account10.1088/1755-1315/95/4/042054 ↗
17. Lekatech, O. (2025, June 27). Why Electric Hammering?. Available online: https://www.lekatech.fi/.
18. Design and performance analysis of a long-stroke electromagnetic double-reel hammer
19. ANN and ANFIS performance prediction models for hydraulic impact hammers10.1016/j.tust.2011.06.004 ↗
20. Comparative studies on the performance of a roadheader, impact hammer and drilling and blasting method in the excavation of metro station tunnels in Istanbul10.1016/j.tust.2009.11.002 ↗
21. Bilgin, N., Yazici, S., and Eskikaya, S. (1996, January 2–5). A model to predict the performance of roadheaders and impact hammers in tunnel drivages. Proceedings of the ISRM EUROCK. ISRM, Turin, Italy.
22. Bilgin, N., Kuzu, C., Eskikaya, S., and Özdemir, L. (1997, January 12–17). Cutting performance of jack hammers and roadheaders in Istanbul Metro drivages. Proceedings of the World Tunnel Congress ‘97. Tunnels for People, Vienna, Austria.
23. The performance prediction of impact hammers from Schmidt hammer rebound values in Istanbul metro tunnel drivages10.1016/S0886-7798(02)00009-3 ↗
24. Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling10.1016/j.tust.2010.06.011 ↗
25. Learning to Break Rocks With Deep Reinforcement Learning10.1109/LRA.2023.3236562 ↗
26. Performance prediction of roadheaders using ensemble machine learning techniques10.1007/s00521-017-3141-2 ↗
27. Performance prediction of impact hammer using ensemble machine learning techniques10.1016/j.tust.2018.07.030 ↗
28. Design study of impact performance of a DTH hammer using PQRSM and numerical simulation10.1007/s12206-019-1052-0 ↗
29. Numerical and experimental investigation on rock breaking performance with hydraulic splitter10.1016/j.tust.2019.103181 ↗
30. Skoczylas, A., Anufriiev, S., and Stefaniak, P. (2020, January 18–24). Oversized ore pieces detection method based on computer vision and sound processing for validation of vibrational signals in diagnostics of mining screen. Proceedings of the SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings, Albena, Bulgaria.10.5593/sgem2020/1.1/s04.101 ↗
31. Ore Volume Measurement Based on 3D Laser Scanning Technique: A Case Study10.4028/www.scientific.net/AMR.610-613.3708 ↗
32. Estimating volume of oversized copper ore chunks in an underground mine using a laser scanner and an RGB camera for hammering efficiency assessment10.1080/17480930.2023.2289302 ↗
33. Volume calculation methods of irregular stone artefacts based on 3D laser scanning technology10.1080/13467581.2023.2182640 ↗
34. Autonomous robotic rock breaking using a real-time 3D visual perception system10.1002/rob.22022 ↗
35. Cárdenas, D., Parra-Tsunekawa, I., Leiva, F., and Ruiz-del Solar, J. (2022). Automatic determination of rock-breaking target poses for impact hammers. Energies, 15.10.3390/en15176380 ↗
36. Skoczylas, A., Stefaniak, P., Anufriiev, S., and Jachnik, B. (2021). Belt conveyors rollers diagnostics based on acoustic signal collected using autonomous legged inspection robot. Appl. Sci., 11.10.3390/app11052299 ↗
37. Shiri, H., Wodecki, J., Ziętek, B., and Zimroz, R. (2021). Inspection robotic UGV platform and the procedure for an acoustic signal-based fault detection in belt conveyor idler. Energies, 14.10.3390/en14227646 ↗
38. Alharbi, F., Luo, S., Zhang, H., Shaukat, K., Yang, G., Wheeler, C.A., and Chen, Z. (2023). A brief review of acoustic and vibration signal-based fault detection for belt conveyor idlers using machine learning models. Sensors, 23.10.3390/s23041902 ↗
39. Acoustic signal based fault detection on belt conveyor idlers using machine learning10.1016/j.apt.2020.04.034 ↗
40. Lalik, K., Dominik, I., Gut, P., Skrzypkowski, K., Korzeniowski, W., and Zagórski, K. (2021). Non-destructive acoustical rock bolt testing system with intelligent filtering in salt mine ‘Wieliczka’. Energies, 14.10.3390/en14175522 ↗
41. A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution10.1016/j.ymssp.2013.08.015 ↗
42. Generalized spectral coherence for cyclostationary signals with α-stable distribution10.1016/j.ymssp.2021.107737 ↗
43. Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment10.1016/j.ymssp.2023.110465 ↗
44. Praveenkumar, T., Saimurugan, M., and Ramachandran, K. (2017). Comparison of vibration, sound and motor current signature analysis for detection of gear box faults. Int. J. Progn. Health Manag., 8.10.36001/ijphm.2017.v8i2.2642 ↗
45. KGHM ZANAM (2025, March 07). Stationary Rock Breaker Type URB/ZS-1R. Available online: https://www.kghmzanam.com/wp-content/uploads/2020/07/URB_ZS_1R_EN.pdf.
46. Lekatech, O. (2025, March 07). LEKA TORAS 35 ELECTRIC HAMMER. Available online: https://www.lekatech.fi/site/assets/files/1/leka_toras_35_flyer.pdf.
47. x-io Technologies Ltd (2025, June 27). NGIMU User Manual. Available online: https://x-io.co.uk/downloads/NGIMU-User-Manual-v1.6.pdf.
48. Bruel & Kjaer (2025, June 27). Type 4188-A-021 ½-inch Free-field Microphone With Type 2671 Preamplifier. Available online: https://www.bksv.com/de/transducers/acoustic/microphones/microphone-set/4188-a-021.
49. Fluke Europe, B.V. (2025, June 27). Fluke i400s AC Current Clamp. Available online: https://www.fluke.com/en/product/accessories/current-clamps/fluke-i400s.
50. GoPro Inc. (2025, June 27). Go Pro Hero 12. Available online: https://gopro.com/en/us/shop/cameras/hero12-black/CHDHX-121-master.html.
51. Samorodnitsky, G., and Taqqu, M.S. (1994). Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance, Chapman & Hall.
52. Weron, R. (2004). Computationally Intensive Value at Risk Calculations, Humboldt-Universität zu Berlin, Center for Applied Statistics and Economics (CASE). Technical report, Papers.
53. Regression-type estimation of the parameters of stable laws10.1080/01621459.1980.10477573 ↗
54. ‘Student’and small sample theory
55. Student (1908). The probable error of a mean. Biometrika, Oxford University Press.10.2307/2331554 ↗
56. Jackman, S. (2009). Bayesian Analysis for the Social Sciences, John Wiley & Sons.10.1002/9780470686621 ↗
57. Erratum to “Rock fragmentation classification applying machine learning approaches”10.4236/eng.2025.171001 ↗