MENG Debiao1,2, YANG Hengfei1,2, YANG Shiyuan3, SU Xiaoyan4, ZHU Shunpeng1
1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731; 2. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan 523808; 3. Faculty of Engineering, University of Porto, Porto 4150-564, Portugal; 4. School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090
[1] LI X Q，SONG L K，BAI G C. Recent advances in reliability analysis of aeroengine rotor system：A review[J]. International Journal of Structural Integrity，2022，13(1)：1-29. [2] 韦新鹏，姚中洋，宝文礼，等. 一种基于主动学习克里金模型的证据理论可靠性分析方法[J]. 机械工程学报，2024，60(2)：356-368. WEI Xinpeng，YAO Zhongyang，BAO Wenli，et al. An evidence theory reliability analysis method based on active learning Kriging model[J]. Journal of Mechanical Engineering，2024，60(2)：356-368. [3] HE Y，DENG Y. MMGET：A Markov model for generalized evidence theory[J]. Computational and Applied Mathematics，2022，41：1-41. [4] MENG D，YANG S，HE C，et al. Multidisciplinary design optimization of engineering systems under uncertainty：A review[J]. International Journal of Structural Integrity，2022，13(4)：565-593. [5] 聂鹏，杨世源，郭永强，等．氢环境下金属材料的疲劳寿命预测：从裂纹的角度[J]．长沙理工大学学报(自然科学版)，2024，21(4)：44-67. NIE Peng，YANG Shiyuan，GUO Yongqiang，et al. Prediction of fatigue life of metallic materials in hydrogen environment：A crack perspective[J]. Journal of Changsha University of Science & Technology (Natural Science)，2024，21(4)：44-67. [6] WANG Z，XIAO F，CAO Z. Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making[J]. Soft Computing，2022，26(19)：9937-9952. [7] QIANG C，DENG Y. A new correlation coefficient of mass function in evidence theory and its application in fault diagnosis[J]. Applied Intelligence，2022，52(7)：7832-7842. [8] LUO C，KESHKEGAR B，ZHU SP，et al. Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering，2022，388：114218. [9] 刘泽清，程鑫，杨旭锋. 基于ALK模型与子集模拟的主动学习可靠性分析方法[J]. 机械强度，2024，46(1)：96-106. LIU Zeqing，CHENG Xin，YANG Xufeng. Active learning reliability analysis method based on ALK model and subset simulation[J]. Journal of Mechanical Strength，2024，46(1)：96-106. [10] 智鹏鹏，汪忠来，李永华，等. 基于RMQGS-APS- Kriging的主动学习结构可靠性分析方法[J]. 机械工程学报，2022，58(16)：420-429. ZHI Pengpeng，WANG Zhonglai，LI Yonghua，et al. An active learning structural reliability analysis method based on RMQGS-APS-Kriging[J]. Journal of Mechanical Engineering，2022，58(16)：420-429. [11] PAPAIOANNOU I，STRAUB D. Combination line sampling for structural reliability analysis[J]. Structural Safety，2021，88：102025. [12] KESHTEGAR B，SEGHIER M E A B，ZHU S P，et al. Reliability analysis of corroded pipelines：Novel adaptive conjugate first order reliability method[J]. Journal of Loss Prevention in the Process Industries，2019，62：103986. [13] HU Z，MANSOUR R，OLSSON M，et al. Second-order reliability methods：A review and comparative study[J]. Structural and Multidisciplinary Optimization，2021，64：3233-3263. [14] 郝鹏，杨浩，陈发鑫，等. 考虑载荷及强度不确定性的结构安全系数精细化设计方法[J]. 机械工程学报，2024，60(13)：182-192. HAO Peng，YANG Hao，CHEN Faxin，et al. A precise design method for structural safety factor considering load and strength uncertainty[J]. Journal of Mechanical Engineering，2024，60(13)：182-192. [15] MENG D，XIE T，WU P，et al. Uncertainty-based design and optimization using first order saddle point approximation method for multidisciplinary engineering systems[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems，Part A：Civil Engineering，2020，6(3)：04020028. [16] XIA Y，HU Y，TANG F，et al. An Armijo-based hybrid step length release first order reliability method based on chaos control for structural reliability analysis[J]. Structural and Multidisciplinary Optimization，2023，66(4)：77. [17] LI Gang，BIN Li，HAO Hu. A novel first-order reliability method based on performance measure approach for highly nonlinear problems[J]. Structural and Multidisciplinary Optimization，2018，57：1593-1610. [18] KESHKEGAR B，CHAKRABORTY S. A hybrid self-adaptive conjugate first order reliability method for robust structural reliability analysis[J]. Applied Mathematical Modelling，2018，53：319-332. [19] ZHAO W，CHEN Y，LIU J. An effective first order reliability method based on Barzilai-Borwein step[J]. Applied Mathematical Modelling. 2020，77：1545-1563. [20] ZHANG D，ZHANG J，YANG M，et al. An enhanced finite step length method for structural reliability analysis and reliability-based design optimization[J]. Structural and Multidisciplinary Optimization，2022，65(8)：231. [21] GHOHANI Arab H，RASHKI M，ROSTAMIAN M，et al. Refined first-order reliability method using cross- entropy optimization method[J]. Engineering with Computers，2019，35：1507-1519. [22] LIU X，LI T，ZHOU Z，et al. An efficient multi-objective reliability-based design optimization method for structure based on probability and interval hybrid model[J]. Computer Methods in Applied Mechanics and Engineering，2022，392：114682. [23] MENG Z，LI G，WANG X，et al. A comparative study of metaheuristic algorithms for reliability-based design optimization problems[J]. Archives of Computational Methods in Engineering，2021，28：1853-1869. [24] ZHU S P，BEHROOZ K，AMINE E M S B，et al. Hybrid and enhanced PSO：Novel first order reliability method- based hybrid intelligent approaches[J]. Computer Methods in Applied Mechanics and Engineering，2022，393：114730. [25] PEDROSO M D. Form reliability analysis using a parallel evolutionary algorithm[J]. Structural Safety，2017，65：84-99. [26] ZHONG C，WANG M，DANG C，et al. First-order reliability method based on Harris hawks optimization for high-dimensional reliability analysis[J]. Structural and Multidisciplinary Optimization，2020，62(4)：1951-1968. [27] YANG K，XUE B，FANG H，et al. Mechanical sensitivity analysis of pipe-liner composite structure under multi- field coupling[J]. Structures，2021，29：484- 493. [28] LIU X，LIU X，ZHOU Z，et al. An efficient multi- objective optimization method based on the adaptive approximation model of the radial basis function[J]. Structural and Multidisciplinary Optimization，2021，63：1385-1403. [29] YU S，WANG Z，LI Y. Time and space-variant system reliability analysis through adaptive Kriging and weighted sampling[J]. Mechanical Systems and Signal Processing，2022，166：108443. [30] WANG W，MA Y，LIU B，et al. Reliability analysis of reusable turbine rotor blisk：An application of parametric modelling method under multi-field coupling[J]. Engineering Failure Analysis，2023，152：107511. [31] AI Q，YUAN Y，JIANG X，et al. Pathological diagnosis of the seepage of a mountain tunnel[J]. Tunnelling and Underground Space Technology，2022，128：104657. [32] LI C，HUANG M，LI W，et al. Optimization of an induction motor for loss reduction considering manufacturing tolerances[J]. Structural and Multidisciplinary Optimization，2022，65(7)：187. [33] LI Y，LI C，GARG A，et al. Heat dissipation analysis and multi-objective optimization of a permanent magnet synchronous motor using surrogate assisted method[J]. Case Studies in Thermal Engineering，2021，27：101203. [34] WANG Z，ZHAO D，GUAN Y. Flexible-constrained time-variant hybrid reliability-based design optimization[J]. Structural and Multidisciplinary Optimization，2023，66(4)：89. [35] LUO C，KESHTEGAR B，ZHU S P，et al. EMCS-SVR：Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering，2022，400：115499. [36] MENG D，YANG S，YANG H，et al. Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty[J]. Ocean Engineering，2024，307：118213. [37] 胡伟飞，廖家乐，郭云飞，等. 基于物理信息神经网络的时变可靠性分析方法[J]. 机械工程学报，2024，60(13)：141-153. HU Weifei，LIAO Jiale，GUO Yunfei，et al. Time-varying reliability analysis method based on physical information neural network[J]. Journal of Mechanical Engineering，2024，60(13)：141-153. [38] CHERID D，BOURAHLA N，LAGHOUB M S，et al. Sensor number and placement optimization for detection and localization of damage in a suspension bridge using a hybrid ANN-PCA reduced FRF method[J]. International Journal of Structural Integrity，2022，13(1)：133-149. [39] 王攀，辛富康，邓亚权，等. 基于自适应神经网络的多模式小失效概率分析方法[J]. 机械工程学报，2022，58(23)：39-50. WANG Pan，XIN Fukang，DENG Yaquan，et al. A multi-mode small failure probability analysis method based on adaptive neural network[J]. Journal of Mechanical Engineering，2022，58(23)：39-50. [40] LI M，SADOUGHI M，HU Z，et al. A hybrid Gaussian process model for system reliability analysis[J]. Reliability Engineering & System Safety，2020，197：106816. [41] WANG Y，ZHANG C，SU Y，et al. Structure optimization of the frame based on response surface method[J]. International Journal of Structural Integrity，2020，11(3)：411-425. [42] ZHOU Y，LU Z，YUN W. Active sparse polynomial chaos expansion for system reliability analysis[J]. Reliability Engineering & System Safety，2020，202：107025. [43] TONG C，ZHANG Q，CUI C，et al. An efficient latent map multi-fidelity Kriging model and adaptive point- selected strategy for reliability analysis with time- consuming simulations[J]. Structural and Multidisciplinary Optimization，2024，67(5)：71. [44] MENG D，YANG H，YANG S，et al. Kriging-assisted hybrid reliability design and optimization of offshore wind turbine support structure based on a portfolio allocation strategy[J]. Ocean Engineering，2024，295：116842. [45] 余萌晨，龙湘云. 基于两阶段主动学习Kriging模型的广义概率区间混合可靠性分析[J]. 机械工程学报，2022，58(6)：274-288. YU Mengchen，LONG Xiangyun. Generalized probabilistic interval hybrid reliability analysis based on two-stage active learning Kriging model[J]. Journal of Mechanical Engineering，2022，58(6)：274-288. [46] YU S，LI Y. Active learning Kriging model with adaptive uniform design for time-dependent reliability analysis[J]. IEEE Access，2021，9：91625-91634. [47] YANG M，ZHANG D，WANG F，et al. Efficient local adaptive Kriging approximation method with single-loop strategy for reliability-based design optimization[J]. Computer Methods in Applied Mechanics and Engineering，2022，390：114462. [48] XIONG Y，SAMPATH S. A fast-convergence algorithm for reliability analysis based on the AK-MCS[J]. Reliability Engineering & System Safety，2021，213：107693. [49] ZHANG X，LU Z，CHENG K. AK-DS：An adaptive Kriging-based directional sampling method for reliability analysis[J]. Mechanical Systems and Signal Processing，2021，156：107610. [50] QIN Qiang，CAO Xiaolei，ZHANG Shengpeng. AWK- TIS：An improved AK-IS based on whale optimization algorithm and truncated importance sampling for reliability analysis[J]. CMES-Computer Modeling in Engineering & Sciences，2023，135(2)：1457-1480. [51] HUANG X，CHEN J，ZHU H. Assessing small failure probabilities by AK-SS：An active learning method combining Kriging and Subset Simulation[J]. Structural Safety，2016，59：86-95. [52] 安永凯，张岩祥，闫雪嫚. 基于自适应多保真度Co-Kriging代理模型的地下水污染源反演识别[J]. 中国环境科学，2024，44(3)：1376-1385. AN Yongkai，ZHANG Yanxiang，YAN Xueman. Inversion and identification of groundwater pollution sources based on adaptive multi-fidelity Co-Kriging surrogate model[J]. China Environmental Science，2024，44(3)：1376-1385. [53] 黎程山，吕达，安小逸，等. 翼身融合水下滑翔机耐压舱固定架多保真度优化设计[J]. 中国舰船研究，2024，19(16)：126-134. LI Chengshan，LYU Da，AN Xiaoyi，et al. Multi-fidelity optimization design of the fixed frame of the pressure cabin of the blended wing-body underwater glider[J]. Chinese Journal of Ship Research，2024，19(16)：126-134. [54] XIAO M，ZHANG G，BREITKOPF P，et al. Extended Co-Kriging interpolation method based on multi-fidelity data[J]. Applied Mathematics and Computation，2018，323：120-131. [55] ULAGANATHAN S，COUCKUYT I，FERRANTI F，et al. Performance study of multi-fidelity gradient enhanced Kriging[J]. Structural and Multidisciplinary Optimization，2015，51：1017-1033. [56] ZHOU Q，WU Y，GUO Z，et al. A generalized hierarchical co-Kriging model for multi-fidelity data fusion[J]. Structural and Multidisciplinary Optimization，2020，62：1885-1904. [57] YI J，CHENG Y，LIU J. A novel fidelity selection strategy-guided multi-fidelity Kriging algorithm for structural reliability analysis[J]. Reliability Engineering & System Safety，2022，219：108247. [58] 杨旭锋，程鑫，刘泽清. 一种融合交叉熵自适应抽样与ALK模型的可靠性分析方法[J]. 机械工程学报，2024，60(16)：73-82. YANG Xufeng，CHENG Xin，LIU Zeqing. A reliability analysis method combining cross entropy adaptive sampling and ALK model[J]. Journal of Mechanical Engineering，2024，60(16)：1-10. [59] ZHU S P，KESHTEGAR B，CHAKRABORTY S，et al. Novel probabilistic model for searching most probable point in structural reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering，2020，366：113027. [60] BARBOSA M P F，RADE D A. Kriging/FORM reliability analysis of rotor-bearing systems[J]. Journal of Vibration Engineering & Technologies，2022，10(6)：2179-2201. [61] XUE J，SHEN B. A novel swarm intelligence optimization approach：Sparrow search algorithm[J]. Systems Science & Control Engineering，2020，8(1)：22-34. [62] XIAO F，PEDRYCZ W. Negation of the quantum mass function for multisource quantum information fusion with its application to pattern classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，45(2)：2054-2070. [63] BICHON J B，ELDRED S M，SWILER P L. Efficient global reliability analysis for nonlinear implicit performance functions[J]. AIAA Journal，2008，46(10)：2459-2468. [64] WANG Z，SHAFIEEZADEH A. ESC：An efficient error-based stopping criterion for Kriging-based reliability analysis methods[J]. Structural and Multidisciplinary Optimization，2019，59：1621-1637. [65] FORRESTER A，SOBESTER A，KEANE A. Engineering design via surrogate modelling：A practical guide[M]. Hoboken：John Wiley & Sons，2008. [66] ZHANG L，LU Z，WANG P. Efficient structural reliability analysis method based on advanced Kriging model[J]. Applied Mathematical Modelling，2015，39(2)：781-793. [67] MENG Z，LI G，YANG D，et al. A new directional stability transformation method of chaos control for first order reliability analysis[J]. Structural and Multidisciplinary Optimization，2017，55：601-612. [68] PIDAPARTHI B，MISSOUM S. A multi-fidelity approach for reliability assessment based on the probability of classification inconsistency[J]. Journal of Computing and Information Science in Engineering，2023，23(1)：011008. [69] CURRIN C，MITCHELL T，MORRIS M，et al. Bayesian prediction of deterministic functions，with applications to the design and analysis of computer experiments[J]. Journal of the American Statistical Association，1991，86(416)：953-963. [70] XIONG S，QIAN P Z G，WU C F J. Sequential design and analysis of high-accuracy and low-accuracy computer codes[J]. Technometrics，2013，55(1)：37-46. [71] SHI Y，LU Z，HE R，et al. A novel learning function based on Kriging for reliability analysis[J]. Reliability Engineering & System Safety，2020，198：106857. [72] ZADEH P M，MODAGHEGHI M. An efficient Bi-level hybrid multi-objective reliability-based design optimization of composite structures[J]. Composite Structures，2022，296：115862.
