Reliability optimization design method based on multi-level surrogate model

• Autorzy: Li Yong-Hua , Liang Xiao-Jia , Dong Si-Hui

Identyfikatory

DOI

10.17531/ein.2020.4.7

• Języki publikacji: EN

Abstrakty

EN

In this work, a genetic-algorithm-based Kriging model with multi-point addition sequence optimization strategy is addressed to make up for the shortcomings of Kriging model with single point criterion. This approach combines the multi-point addition strategy with genetic algorithm to enable the Kriging model to efficiently capture the globally optimal solution. Based on this, a multi-level surrogate method is presented by employing a local surrogate model to modify the Kriging global surrogate model, and then applied to design optimization to improve the accuracy and efficiency of global optimization. Meanwhile, a reliability design optimization method based on multi-level surrogate model is studied by dealing with the reliability constraints with an adaptive reliability penalty function. Numerical examples show that the proposed method can find the optimal solution of the object problem with the least calculation cost under the condition of satisfying the reliability constraint.

Słowa kluczowe

EN

Kriging model; reliability-based optimization; multi-level surrogate model; adaptive dynamic penalty function

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2020
• Tom: Vol. 22, no. 4
• Strony: 638--650
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Li Yong-Hua

• School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Liaoning, 116028, P.R. China

autor

Liang Xiao-Jia

• CRRC Changchun Railway Vehicle Co., Ltd, Jilin, 130062, P. R. China

autor

Dong Si-Hui

• School of Traffic and Transportation Engineering, Dalian Jiaotong University, Liaoning, 116028, P.R. China

Bibliografia

• 1. Ahmadi B, Nariman-zadeh N, Jamali A. Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory. Engineering Optimization 2016; 49(6): 932–947, https://doi.org/10.1080/0305215X.2016.1218641.
• 2. Anuj K, Sangeeta P, Mangey R. System reliability optimization using gray wolf optimizer algorithm. Quality and Reliability Engineering International 2017; 33(7): 1327-1335, https://doi.org/10.1002/qre.2107.
• 3. Buchheim C, Kurtz J. Robust combinatorial optimization under convex and discrete cost uncertainty. EURO Journal on Computational Optimization 2018; 6: 211–238, https://doi.org/10.1007/s13675-018-0103-0.
• 4. Chocat R, Beaucaire P, Debeugny L, Lefebvre J P, Sainvitu C, Breitkopf P, Wyart E. Damage tolerance reliability analysis combining Kriging regression and support vector machine classification. Engineering Fracture Mechanics 2019; 216: 1–13, https://doi.org/10.1016/j.engfracmech.2019.106514.
• 5. Di Somma M, Yan B, Bianco N, Graditi G, Luh P B, Mongibello L, Naso V. Multi-objective design optimization of distributed energy systems through cost and energy assessments. Applied Energy 2017, 204, 1299–1316, https://doi.org/10.1016/j.cma.2016.10.048.
• 6. Emre D, Ali R Y. A new hybrid approach for reliability-based design optimization of structural components. Materials Testing 2019; 61(2):111–119, https://doi.org/10.3139/120.111291.
• 7. Freier L, Wiechert W, Von Lieres E. Kriging with trend functions nonlinear in their parameters: theory and application in enzyme kinetics. Engineering in Life Sciences 2017; 17(8): 1–10, https://doi.org/10.1002/elsc.201700022.
• 8. Gao H F, Wang A, Zio E, Ma W. Fatigue strength reliability assessment of turbo-fan blades by Kriging-based distributed collaborative response surface method. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21(3): 530–538, https://doi.org/10.17531/ein.2019.3.20.
• 9. Gaspar B, Teixeira A P, Guedes Soares C. Adaptive surrogate model with active refinement combining Kriging and a trust region method. Reliability Engineering & System Safety 2017; 165: 277–291, https://doi.org/10.1016/j.ress.2017.03.035.
• 10. Haeri A, Fadaee M J. Efficient reliability analysis of laminated composites using advanced Kriging surrogate model. Composite Structures 2016; 149: 26–32, https://doi.org/10.1016/j.compstruct.2016.04.013.
• 11. Hawchar L, Soueidy C P E, Schoefs F. Global kriging surrogate modeling for general time-variant reliability-based design optimization problems. Structural and Multidisciplinary Optimization 2018; 58(3): 955–968, https://doi.org/10.1007/s00158-018-1938-y.
• 12. Heidari A A, Faris H, Aljarah I, Mirjalili S. An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing 2018; 23: 7941–7958, https://doi.org/10.1007/s00500-018-3424-2.
• 13. Ismail H Y, Singh M, Darwish S, Kuhs M, Shirazian S, Croker D M. Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation. Powder Technology 2019; 343: 568–577, https://doi.org/10.1016/j.powtec.2018.11.060.
• 14. Korta J A, Mundo D. Multi-objective micro-geometry optimization of gear tooth supported by response surface methodology. Mechanism & Machine Theory 2017; 109: 278–295, https://doi.org/10.1016/j.mechmachtheory.2016.11.015.
• 15. Lara H, Charbel-Pierre E S, Franck S. Principal component analysis and polynomial chaos expansion for time-variant reliability problems. Reliability Engineering & System Safety 2017; 167: 406–416, https://doi.org/10.1016/j.ress.2017.06.024.
• 16.Li Y F, Huang H Z, Mi J, Peng W, Han X. Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability. Annals of Operations Research 2019; https://doi.org/10.1007/s10479-019-03247-6.
• 17.Li Y H, Hu M G, Wang F. Fatigue life analysis based on six sigma robust optimization for pantograph collector head support. Advances in Mechanical Engineering 2016; 8(11): 1–9, https://doi.org/10.1177/1687814016679314.
• 18.Li Y H, Li Y H, Wang Y D, Wang J. Structural optimization–based fatigue durability analysis of electric multiple units cowcatcher. Advances in Mechanical Engineering 2017; 9(8): 1–10, https://doi.org/10.1177/1687814017726294.
• 19. Martínez-Frutos J, Herrero-Pérez D. Kriging-based infill sampling criterion for constraint handling in multi-objective optimization. Journal of Global Optimization 2015; 64(1): 97–115, https://doi.org/10.1007/s10898-015-0370-8.
• 20. Mi J H, Li Y F, Yang Y J, Peng W W, Huang H Z. Reliability assessment of complex electromechanical systems under epistemic uncertainty. Reliability Engineering & System Safety 2016; 152: 1–15, https://doi.org/10.1016/j.ress.2016.02.003.
• 21. Mi J, Beer M, Li Y F, Broggi M, Cheng Y. Reliability and importance analysis of uncertain system with common cause failures based on survival signature. Reliability Engineering & System Safety 2020; 106988, https://doi.org/10.1016/j.ress.2020.106988.
• 22.Narayanakumar S, Raja, K. A BP artificial neural network model for earthquake magnitude prediction in himalayas. Circuits and Systems 2016; 7: 3456–3468, https://doi.org/10.4236/cs.2016.711294.
• 23. Ozcanan S, Atahan A O. RBF surrogate model and EN1317 collision safety-based optimization of two guardrails. Structural and Multidisciplinary Optimization 2019; 60: 343–362, https://doi.org/10.1007/s00158-019-02203-z.
• 24. Stern R E, Song J, Work D B. Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity. Reliability Engineering & System Safety 2017; 164: 1–9, https://doi.org/10.1016/j.ress.2017.01.021.
• 25. Sundar V S, Shields M D. Surrogate-enhanced stochastic search algorithms to identify implicitly defined functions for reliability analysis. Structural Safety 2016; 62: 1–11, https://doi.org/10.1016/j.strusafe.2016.05.001.
• 26. Vatteri A P, Balaji Rao, K, Bharathan, A M. Time-variant reliability analysis of RC bridge girders subjected to corrosion-shear limit state. Structural Concrete, 2016; 17(2), 162–174, https://doi.org/10.1002/suco.201500081.
• 27. Yadav R N. A hybrid approach of Taguchi-response surface methodology for modeling and optimization of duplex turning process. Measurement 2017; 100: 131–138, https://doi.org/10.1016/j.measurement.2016.12.060.
• 28. Yu J T. Research on wing design based on reliability optimization. Shenyang Aerospace University, 2015.
• 29. Yu S, Wang Z L, Zhang K W. Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty. Reliability Engineering and System Safety 2018; 170: 45–52, https://doi.org/10.1016/j.ress.2017.10.006.
• 30. Zhang Y, Lin F Y. Optimum design of collision reliability of aluminum foam filled thin wall structure. Journal of Mechanical Engineering 2011; 47(22): 93–99, https://doi.org/ 10.3901/JME.2011.22.093.
• 31. Zhi P P, Li Y H, Chen B Z, Li M, Liu G N. Fuzzy optimization design-based multi-level response surface of bogie frame. International Journal of Structural Integrity 2019; 10(2): 134–148, https://doi.org/10.1108/IJSI-10-2018-0062.
• 32. Zhou W, Fang J. Application of improved response surface method in reliability optimization of body tubes. Mechanical Science and Technology 2016; 2: 176–181, https://doi.org/10.13433/j.cnki.1003-8728.2016.0203.
• 33. Zhu L, Zhang Y, Zhang R, Zhang P. Time-dependent reliability of spur gear system based on gradually wear process. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20 (2): 207–218, https://doi.org/10.17531/ein.2018.2.05.
• 34. Zhu S P, Huang H Z, Peng W, Wang H K, Sankaran M. Probabilistic Physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering & System Safety 2016; 146: 1–12, https://doi.org/10.1016/j.ress.2015.10.002.

Uwagi

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