Hardness identification of rock based on multi-sensor information fusion during the process of roadway tunnelling

• Autorzy: Wang H. , Lu S. , Huang M. , Zhao X.

Identyfikatory

DOI

10.24425/bpasts.2020.135381

• Języki publikacji: EN

Abstrakty

EN

In this paper, a new dynamic model was proposed for identifying the rock hardness during the process of roadway tunnelling, thereby regulating the speed of the driving motor and the torque of the cutting head. The presented identification model establishes a multi-information feature database containing vibration signals in the y-axis, acoustic emission signals, cutting current signals, and temperature signals. Subsequently, we obtain the membership functions (MFs) of the given multiple signals with the amount of feature samples according to the principle of minimum fuzzy entropy. Furthermore, a rock hardness identification model was established based on multi-sensor information fusion and Dempster-Shafer (D-S) evidence theory. To prove the accuracy of the proposed model, an identification experiment was carried out through the cutting of a poured mixed rock specimen with five grades of hardness. As a result, the proposed identification model recognizes the rock hardness accurately for fifteen sampling points, which indicates the significance of the method with regard to the dynamic identification of rock hardness during the process of roadway tunnelling, and further provides data support for adjusting the speed of the cutting head adaptively, thereby achieving high efficiency tunnelling.

Słowa kluczowe

EN

rock hardness identification; membership function; information fusion; Dempster-Shafer

PL

identyfikacja twardości skały; członkostwo; fuzja informacji; teoria Dempstera-Shafera

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 6
• Strony: 1301--1309
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Wang H.

• Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin Guangxi, China

autor

Lu S.

• Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin Guangxi, China

autor

Huang M.

• Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin Guangxi, China

autor

Zhao X.

• School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, China

Bibliografia

• [1] H.M. Mao, K. Chen, and H.H. Feng, “Rock-breaking mechanism of TBM with different types of cutter tools and estimation of thrust force”, Chinese J. Geo-tech. Eng. 35 (9), 1627–1633 (2013).
• [2] L.J. Zhao, X.N. Liu, and T. Cao, “Horizontal swing movement parameters optimization design of longitudinal road header”, J. China Coal Soc. 37 (12), 2112–2117 (2012).
• [3] K.Z. Song, H.X. Yang, K. An, et al., “Shield tunnelling rate prediction model for complex rock stratum”, J. Highw. Transp. Res. Dev. 25 (11), 105–108 (2008).
• [4] J.J. Yang, H. Jiang, X.D. Ji, et al., “Vibration identification method of coal and rock hardness based on wavelet packet features”, Coal Sci. Technol. 43 (12), 114–117, 179 (2015).
• [5] J.J. Yang, S.C. Fu, H. Jiang, et al., “Recognition of cutting hardness of coal rock properties based on fuzzy criteria”, J. China Coal Soc. 40 (S2), 540–545 (2015).
• [6] W.K. Utt., “Neural network technology for strata strength characterization”, in Proceedings of the International Joint Conference on Neural Networks 6 (1), 3806–3809 (1999).
• [7] S. Kahraman, J. Rostami, and A. Naeimipour, “Review of ground characterization by using instrumented drills for underground mining and construction”, Rock Mech. Rock Eng. 49 (2), 585–602 (2016).
• [8] X.H. Li and L.L. Jiang, “Excavator loading simulation for hardrock cutting”, Chinese J. Constr. Mach. 6 (4), 415–417, (2008).
• [9] T. Szwedzicki, “Indentation hardness testing of rock”, Int. J. Rock Mech. Min. Sci. 35 (6), 825–829 (1998).
• [10] E. Yaşar and Y. Erdoğan, “Estimation of rock physicomechanical properties using hardness methods”, Eng. Geol. 71 (34), 281–288 (2004).
• [11] F.I. Shalabi, E.J. Cording, and O.H. Al-Hattamleh, “Estimation of rock engineering properties using hardness tests”, Eng. Geol. 90 (34), 138–147 (2007).
• [12] J. van Eldert, H. Schunnesson, D. Johansson, et al., “Application of Measurement while drilling technology to predict rock mass quality and rock support for tunnelling”, textitRock Mech. Rock Eng. 53 (3), 1349–1358 (2020).
• [13] X.H. Li, Y. He, L. Jiao, et al., “Vertical random vibration response and optimization of cutting head based on parameter identification”, China Mech. Eng. 26 (6), 818–823 (2015).
• [14] Q. Zhang, H.J. Wang, Z. Wang, et al., “Analysis of coal-rock’s cutting characteristics and flash temperature for peak based on infrared thermal image testing”, Chinese J. Sens. Actuators 29 (5), 686–692 (2016).
• [15] X.Y. Wang, Z.W. Jiang, Y.L. Yu, et al., “Research on multi-information fusion technique for diagnosis of urban gas pipeline leakage”, China Saf. Sci. J. 24 (6), 165–170 (2014).
• [16] J.H. Hu, Z.H. Yu, X.S. Zhai, et al., “Research of decision fusion diagnosis of aero-engine rotor fault based on improved D-S theory”, Acta Aeronaut. ETA Astronaut. Sinica. 35 (2), 436–443 (2014).
• [17] B. Wyrwołof, “Linguistic decomposition technique based on partitioning the knowledge base of the fuzzy inference system”, Bull. Pol. Ac.: Tech. 56 (1), 71–76 (2008).
• [18] A. Mreła, O. Sokolov, and W. Urbaniak, “The method of learning outcomes assessment based on fuzzy relations”, Bull. Pol. Ac.: Tech. 67 (3), 527–533 (2019).
• [19] H.J. Wang and Q. Zhang, “Dynamic identification of coal-rock interface based on adaptive weight optimization and multi-sensor information fusion”, Inf. Fusion. 51 (11), 114–128 (2019).
• [20] X. Zhang and L.H. Mou, “Research on compound protection for mine power network based on information fusion”, J. China Coal Soc. 37 (11), 1947–1952 (2012).
• [21] X.M. Liu, L.H. Mou, and X. Zhang, “Failure diagnosis for storage-capacitor in permanent magnetic actuator of flame proof switch gear based on information fusion”, J. China Coal Soc. 39 (10), 2121–2127 (2014).
• [22] A.F. Zhu, Z.R. Jing, W.J. Chen, et al., “Data fusion based on AN-FIS and evidence theory”, Journal of North University of China (Natural Science Edition) 30 (1), 74–79 (2009).
• [23] J.C. Zhang, S.P. Zhou, Y. Li, et al., “Improved D-S evidence theory for pipeline damage identification”, J. Vibroeng. 17 (7), 3527–3537 (2015).
• [24] Y.L. Guo, Y. Liu, Q.Y. Du, et al., “The identification research of emergency treatment technology for sudden heavy metal pollution accidents in drainage basin based on D-S evidence theory”, Water Sci. Technol. 80 (12), 2392–2403 (2020).
• [25] T. Chen, L. Chen, Y.F. Cai, et al., “Estimation of vehicle sideslip angle via pseudo-multisensor information fusion method”, Bull. Pol. Ac.: Tech. 25 (3), 499–516 (2018).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).