Abstract
The ability of an engineering system or products completing the prescribed functions under the prescribed condition and within the prescribed time is called reliability. In the design stage, prescribed functions of an engineering system usually can be expressed as the function of input variable x, which usually can be called the state function g(X). The value of the state function can judge whether functions of an engineering system can meet the design requirements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cong D (1996) Important sampling based reliability analysis for non-linear system. Strength Environ 2:60–64. (董聪. 非线性系统可靠性分析的重要抽样法. 强度与环境, 1996, 2, pp. 60–64.)
Cruse TA (2001) Issues and strategies in probabilistic structural modeling. In: Proceedings 6th national turbine engine high cycle fatigue conference
Cui WC (1990) Uncertainty analysis in structural safety assessment. PhD thesis, Department of Civil Engineering, University of Bristol
Du L (2006) Reliability-based and possibility-based design optimization using inverse analysis methods. PhD thesis of graduate college of the university of Iowa
Faravelli L (1989) Response surface approach for reliability analysis. J Eng Mech 115(12)
Gaofeng R, Shengying Z (2012) Rapid analysis method of landing point error of Mars landing mission. J Harbin Inst Technol 44(7):14–20. (任高峰, 朱圣英. 火星着陆任务落点误差快速分析方法. 哈尔滨工业大学学报, Vol.44, No.7, 2012, pp. 14–20.)
Ghisu T, Parks GT, Jarrett JP, Clarkson PJ (2008) Robust design optimisation of gas turbine compression systems. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. Victoria, British Columbia Canada
Ghisu T, Parks GT, Jarrett JP, Clarkson PJ (2010) Adaptive polynomial chaos for gas turbine compression systems performance analysis. AIAA J 48(6):1156–1170
Hammersley JM, Handscomb DC (1964) Monte Carlo methods. Methuen, London
Harbitz A (1986) An efficient sampling method for probability of failure calculation. Struct Saf 3(1):109–115
Hasofer AM, Lind NC (1974) Exact and invariant second-moment code format. J Eng Mech Div 100(EMI):111–121
Liu BD, Iwamura K (1998) Chance constrained programming with fuzzy parameters[J]. Fuzzy Sets Syst 94(2):227–237
Liu BD (1999) Uncertain Programming[M]. New York, Wiley
Liu B (2000) Dependent-chance programming in fuzzy environments. Fuzzy Sets Syst 109:97–106
Liu B, Haftka RT, Akgun MA (2000) Two-level composite wing structural optimization using response surfaces. Struct Multi Optim 20(2):87–96
Liu D, Choi KK, Youn BD, Gorsich D (2006) Possibility-based design optimization method for design problems with both statistical and fuzzy input data. Trans ASME 128:928–935
Lurati LB (2008) Robust airfoil design under uncertain operation conditions using stochastic collocation. In: 46th AIAA aerospace sciences meeting and exhibit. Reno, Nevada
Madsen HO, Krenk S, Lind NC (1986) Methods of structural safety. NJ, Prentice-Hall, Englewood Cliffs
Manan A, Cooper JE (2008) Uncertainty of composite wing aeroelastic behaviour. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. Victoria, British Columbia Canada
Manan A, Cooper JE (2009) Robust design of composite wings for gust response. In: 50th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference < br > 17th. Palm Springs, California
Manan A, Cooper J (2009) Design of composite wings including uncertainties:a probabilistic approach. J Aircraft 46(2):601–607
Melchers RE (1989) Importance sampling in structural system. Safety 6(1):3–10
Moffitt BA (2010) A methodology for the validated design space exploration of fuel cell powered unmanned aerial vehicles. PhD thesis of Georgia Institute of Technology
Myers RH, Montgomery DC (1995) Response surface methodology. Wiley, New York
Oladyshkin S, Nowak W (2012) Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliab Eng Syst Saf Elsevier 106:179–190. https://doi.org/10.1016/j.ress.2012.05.002
Palle TC, Michael JB (1982) Structural reliability theory and its applications. Springer, Berlin, Heidelberg
Qu XY, Venkataraman S, Haftka R et al. (2001) Reliability, weight, and cost tradeoffs in the design of composite laminates for cryogenic environments[C]. In: Proceedings of the AIAA/ASME/ASCE/AHS/ASC 42nd structures, structural dynamics and materials conference. Seattle, Washington
Rubinsrein RY (1981) Simulation and Monte Carlo methods. Wiley, New York
Saliby E (1990) Descriptive sampling: a better approach to monte carlo simulation. J Opl Res Soc 41(12):1133–1142
Schueller GL, Bucher CG, Bourgund U, Ouypornprasert W (1987) On efficient computational schemes to calculate failure probabilities. In: Lin YK, Schueller (eds) Stochastic structural mechanics, lecture notes in engineering, vol 31. Springer, New York, pp 388–410
Shaojuan S (2007) Research and application of uncertainty analysis method for coastal dry bulk shipping. PhD thesis of Wuhan technology university. (苏绍娟. 沿海干散货船舶运输的不确定性分析方法研究及应用. 武汉理工大学博士论文. 2007.)
Shisong M, Jinglong W, Xiaolong P (1998) Advanced mathematical statistic. Higher education press and Springer. (茆诗松,王静龙,濮晓龙. 高等数理统计. 高等教育出版社&斯普林格出版社, 1998.)
Ting P, Yunqing Z, Jinglai W (2011) Uncertainty analysis of flexible multi-body system based on polynomial chaos method. China Mech Eng 22(19):2341–2348. (皮霆, 张云清, 吴景铼. 基于多项式混沌方法的柔性多体系统不确定性分析. 中国机械工程,第22卷第19期, 2011, pp. 2341–2348.)
Wei Z, Weicheng C (1997) Direct integral method for structural reliability calculation. J. Shanghai Jiao Tong Univer 31(2):114–116. (张伟,崔维成. 结构可靠性计算的直接积分法. 上海交通大学学报, 第31卷, 第2期, 1997, pp. 114–116.)
Wiener N (1938) The homogeneous chaos. Am J Math 60(4):897–936. https://doi.org/10.2307/2371268
Xiaoyu J (2006) Network optimization under uncertain environment. PhD thesis of Tsinghua University. (计小宇. 不确定环境下的网络优化. 清华大学博士论文, 2006.)
Xiu D (2010) Numerical methods for stochastic computations: a spectral method approach. Princeton University Press. ISBN 978-0-691-14212-8
Xiu D, Karniadakis GE (2002) The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput 24(2):619–644. https://doi.org/10.1137/S1064827501387826
Youn BD, Choi KK (2006) Selecting probabilistic approaches for reliability-based design optimization. AIAA J 42(1)
Youn BD, Choi KK, Gu L, Yang R (2004) Reliability-based design optimization for crashworthiness of side impact. J Struct Multi Optim 27(3):272–283
Youn BD, Choi KK, Du L (2005a) Adaptive probability analysis using a enhanced hybrid mean valued method. Struct Multi Optim 29(2):134–148
Youn BD, Choi KK, Du L (2005b) Enriched performance measure approach for reliability-based design optimization. AIAA J 43(4):874–884
Youn BD, Wang P, Xi Z (2007) Complementary interaction method (CIM) for system reliability analysis. In: 48th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference.
Yufu N (2006) Intelligent algorithm based on simulation and its application. PhD thesis of Tianjin University. (宁玉富. 基于模拟的智能算法及其应用. 天津大学博士论文, 2006.)
Zhao LY, Dawes WN, Parks G, Jarrett JP (2009) Robust airfoil design with respect to boundary layer transition. In: 50th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference—17th. Palm Springs, California
Zhu P, Zhang Y, Chen GL (2011) Metamodeling development for reliability-based design optimization of automotive body structure. Comput Ind 62:729–741
Ziha K (1995) Descriptive sampling in structural safety. Struct Saf 17:33–41
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Zhejiang Science and Technology Publishing House Co., Ltd. and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Pan, B., Cui, W. (2020). The Analysis of System Reliability. In: Multidisciplinary Design Optimization and Its Application in Deep Manned Submersible Design. Ocean Engineering & Oceanography, vol 13. Springer, Singapore. https://doi.org/10.1007/978-981-15-6455-0_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-6455-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6454-3
Online ISBN: 978-981-15-6455-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)