Study on reliability of emergency braking performance of high-speed and heavy-load monorail crane

• Autorzy: Zhang Jiarong , Lu Hao , Jiang Hui , Miao Yun , Shi Zhiyuan

Identyfikatory

DOI

10.17531/ein/174820

• Języki publikacji: EN

Abstrakty

EN

The reliability of monorail crane braking system has an important influence on thebraking safety. The high speed and heavy load operation poses a great challenge to the braking safety,and it is necessary to evaluate its braking reliability accurately and efficiently. Firstly, the dynamic performance and thermal-mechanical coupling characteristics of high-speed and heavy-loadmonorail crane under different braking parameters were analyzed. Secondly, the random response model of braking distance and braking temperature was established by combining the design of experiment method (DoE) and Dendrite Net (DD). Finally, the high-order momentsaddlepoint approximation (SPA) method was used to evaluate the emergency braking reliability of the monorail crane. Theresultscan provide a reference for the selection of key parameters and the evaluation of braking safety of the monorail crane braking system under high-speed and heavy-load conditions.

Słowa kluczowe

EN

monorail crane; braking system; reliability; surrogate model

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 1
• Strony: art. no. 174820
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Zhang Jiarong

• School of Mechatronic Engineering, China University of Mining and Technology, China

autor

Lu Hao

• School of Mechatronic Engineering, China University of Mining and Technology, China
• Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, China

• https://orcid.org/0000-0002-5188-1624

autor

Jiang Hui

• School of Mechatronic Engineering, China University of Mining and Technology, China

autor

Miao Yun

• School of Mechatronic Engineering, China University of Mining and Technology, China

autor

Shi Zhiyuan

• China Mining Products Safety Approval and Certification Center, China

Bibliografia

• 1. BlatmanG, SudretB. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probabilistic Engineering Mechanics 2010;25(2): 183-197, https://doi.org/10.1016/j.strusafe.2019.101905.
• 2. Cheng K, Lu Z Z. Structural reliability analysis based on ensemble learning of surrogate models.Structural Safty2020;83: 101905, https://doi.org/10.1016/j.strusafe.2019.101905.
• 3. Dammak K, Baklouti A, El Hami A. Optimal reliable design of brake disk using a Kriging surrogate model. Mechanics of AdvancedMaterials and Structures 2022;29(28):7569-7578, https://doi.org/10.1080/15376494.2021.2002983.
• 4. DengJ, GuD S, LiX B,Yue Z Q. Structural reliability analysis for implicit performance functions using artificial neural network. Structural Safety 2005;27(1): 25-48, https://doi.org/10.1016/j.ijsolstr.2005.05.055.
• 5. DuX P, SudjiantoA. First-order saddlepoint approximation for reliability analysis. Aiaa Journal 2004;42(6): 1199-1207, https://doi.org/10.2514/1.3877.
• 6. DuX P. System reliability analysis with saddlepoint approximation. Structural and Multidisciplinary Optimization 2010;42(2): 193-208, https://doi.org/10.1007/s00158-009-0478-x.
• 7. EchardB, GaytonN, LemaireM. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation. Structural Safety 2011;33(2): 145-154, https://doi.org/10.1016/j.strusafe.2011.01.002.
• 8. ForresterA I J, KeaneA J. Recent advances in surrogate-based optimization. Progress in Aerospace Sciences 2009;45(1-3): 50-79, https://doi.org/10.1016/j.paerosci.2008.11.001.
• 9. GoguC, PassieuxJ C. Efficient surrogate construction by combining response surface methodology and reduced order modeling. Structural and Multidisciplinary Optimization 2013; 47(6): 821-837, https://doi.org/10.1007/s00158-012-0859-4.
• 10. GuoS X. An efficient third-moment saddlepoint approximation for probabilistic uncertainty analysis and reliability evaluation of structures.; 38(1): 221-232, https://doi.org/10.1016/j.apm.2013.06.026.
• 11. GuoY Q, LvZ Y. An uncertainty analysis and reliability-based multidisciplinary design optimization method using fourth-moment saddlepoint approximation. Cmes-Computer Modeling in Engineering & Sciences 2023;134(3): 1855-1870, https://doi.org/10.32604/cmes.2022.022211.
• 12. GuoZ, BaiG. Application of Least Squares Support Vector Machine for Regression to Reliability Analysis. Chinese Journal of Aeronautics2009;22(2): 160-166, https://doi.org/10.1016/S1000-9361(08)60082-5.
• 13. Hu Z, Nannapaneni S; Mahadevan S. Efficient Kriging surrogate modeling approach for system reliability analysis.AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing2017;31(2): 143-160, https://doi.org/10.1017/S089006041700004X.
• 14. HuangB Q, DuX P. Uncertainty analysis by dimension reduction integration and saddlepoint aporoximations. Journal of Mechanical Design 2006;128(1): 26-33, https://doi.org/10.1115/1.2118667.
• 15. Huang X Z, Liu Y, Zhang Y M, Zhang X F. Reliability analysis of structures using stochastic response surface method and saddlepoint approximation. Structural and Multidisciplinary Optimization 2017; 55(6): 2003-2012, https://doi.org/10.1007/s00158-016-1617-9.
• 16. HuangX Z, ZhangY M. Reliability-sensitivity analysis using dimension reduction methods and saddlepoint approximations. International Journal for Numerical Methods in Engineering 2013;93(8): 857-886, https://doi.org/10.1002/nme.4412.
• 17. Kaymaz I. Application of kriging method to structural reliability problems.Structural Safety 2005; 27(2): 133-151, https://doi.org/10.1016/j.strusafe.2004.09.001.
• 18. LiHS. Reliability-based design optimization via high order response surface method. Journal of Mechanical Science and Technology 2013;27(4): 1021-1029, https://doi.org/10.1007/s12206-013-0227-3.
• 19. Liu G, Wang J. Dendrite net: a white-box module for classification, regression, and system identification. Ieee Transactions on Cybernetics2021; 52(12): 13774-13787, https://doi.org/10.1109/TCYB.2021.3124328.
• 20. LuH, Cao S, ZhuZC, Zhang Y M. An improved high order moment-based saddlepoint approximation method for reliability analysis. Applied Mathematical Modelling2020;82:836-847, https://doi.org/10.1016/j.apm.2020.02.006.
• 21. Lyu H, Shangguan W B, Yu D J. A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. Mechanical Systems and Signal Processing 2017;93:281-298, https://doi.org/10.1016/j.ymssp.2017.02.012.
• 22. Lyu H, Yu D J. Brake squeal reduction of vehicle disc brake system with interval parameters by uncertain optimization. Journal of Sound and Vibration2014;333(26):7313-7325, https://doi.org/10.1016/j.jsv.2014.08.027.
• 23. MarelliS, SudretB. An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis. Structural Safety 2018;75: 67-74, https://doi.org/10.1016/j.strusafe.2018.06.003.
• 24. RenC, AouesY, LemosseD, De Cursi. Ensemble of surrogates combining Kriging and Artificial Neural Networks for reliability analysis with local goodness measurement. Structural Safety 2022;96:102186, https://doi.org/10.1016/j.strusafe.2022.102186.
• 25. Ren F B, Chen G A, Lu H, Cao S. The probabilistic modeling and reliability analysis of brake shoes for hoist disc brake with correlated failure modes. Advances in Mechanical Engineering 2020;12(6):1-12, https://doi.org/10.1177/1687814020934596.
• 26. TvedtL. Second Order Reliability by an Exact Integral.Berlin, Heidelberg,Springer Berlin Heidelberg:1989.https://doi.org/10.1007/978-3-642-83828-6_26
• 27. XiangZ, ChenJ, BaoY, Li H. An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mechanical Systems and Signal Processing 2020;140:106684, https://doi.org/10.1016/j.ymssp.2020.106684.
• 28. Yang Z, Pak U, Kwon C, Zhang Y M. A reliability-based robust optimization design for the drum brake using adaptive Kriging surrogate model. Quality and Reliability Engineering International 2023;39(1):454-471, https://doi.org/10.1002/qre.3230.
• 29. Yang Z, Pak U, Kwon C. Vibration reliability analysis of drum brake using the artificial neural network and important sampling method. Complexity 2021, https://doi.org/10.1155/2021/5517634.
• 30. Yang Z, Pak U, Yan Y, Kwon C. Reliability-Based Robust Optimization Design for vehicle drum brake considering multiple failure modes. Structural and Multidisciplinary Optimization 2022;65(9):229-246, https://doi.org/10.1007/s00158-022-03349-z.
• 31. Yuan R, Meng, D B, Li, H Q. Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis. Proceedings of The Institution of Mechanical Engineers Part O-Journal of Risk and Reliability2017;230(6): 570-578, https://doi.org/10.1177/1748006X16673500.
• 32. Zhang N, Jiang G J, Wu D W, Chen H X, Wu J X. Fatigue reliability analysis of the brake pads considering strength degradation. Eksploatacja I Niezawodnosc-Maintenance and Reliability 2020;22(4):620-626, https://doi.org/10.17531/ein.2020.4.5.
• 33. ZhouQ Y, LiZ L, FanW L, Ang A H S, Liu R Y. System reliability assessment of deteriorating structures subjected to time-invariant loads based on improved moment method. Structural Safety 2017;68: 54-64, https://doi.org/10.1016/j.strusafe.2017.05.006.
• 34. ZhouY C, LuZ Z. An enhanced Kriging surrogate modeling technique for high-dimensional problems. Mechanical Systems and Signal Processing 2020;140: 106687, https://doi.org/10.1016/j.ymssp.2020.106687.