Citation: | GONG Xiangrui, LYU Zhenzhou, LIU Hui, et al. Uncertainty analysis of failure of dynamic system and its efficient algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1460-1469. doi: 10.13700/j.bh.1001-5965.2016.0533(in Chinese) |
In order to study the failure of dynamic system when the failure rates of components are uncertain, a new method is proposed to analyze the system failure probability when function time is given and function time when the threshold of failure probability is shown in system. Meanwhile, a new importance measure technique is developed to estimate the impact of components' failure rates on system failure probability and function time in dynamic system. In this paper, the Monte Carlo procedure is given to solve the proposed indices. The fractional moments-based maximum entropy method is used to obtain failure probability density function in system efficiently. An efficient technique with multiplication dimensionality reduction is developed to estimate two importance measure indices. Valve control system and civil aircraft electro-hydraulic actuator system are presented to illustrate the rationality and efficiency of the proposed method.
[1] |
PARRY G W, WINTER P W.Characterization and evaluation of uncertainty in probabilistic risk analysis.Nuclear Safety, 1981, 22(1):251-263. https://www.mendeley.com/research-papers/characterization-evaluation-uncertainty-probabilistic-risk-analysis/
|
[2] |
HOFFMAN F O, HAMMONDS J S.Propagation of uncertainty in risk assessments:The need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability.Risk Analysis, 1994, 14(5):707-712. doi: 10.1111/risk.1994.14.issue-5
|
[3] |
ELDRED M S, SWILER L P, TANG G.Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation.Reliability Engineering & System Safety, 2011, 96(9):1092-1113. https://www.researchgate.net/publication/251621497_Mixed_aleatory-epistemic_uncertainty_quantification_with_stochastic_expansions_and_optimization-based_interval_estimation
|
[4] |
KELLY E J, CAMPBELL K.Separating variability and uncertainty in environmental risk assessment—Making choices.Human and Ecological Risk Assessment, 2000, 6(1):1-13. doi: 10.1080/10807030091124419
|
[5] |
BARALDI P, ZIO E, COMPARE M.A method for ranking components importance in presence of epistemic uncertainties.Journal of Loss Prevention in the Process Industries, 2009, 22(5):582-592. doi: 10.1016/j.jlp.2009.02.013
|
[6] |
ZAFIROPOULO E P, DIALYNAS E N.Reliability and cost optimization of electronic devices considering the component failure rate uncertainty.Reliability Engineering & System Safety, 2004, 84(3):271-284. http://www.sciencedirect.com/science/article/pii/S0951832003002734
|
[7] |
BLANKS H S.Arrhenius and the temperature dependence of non-constant failure rate.Quality and Reliability Engineering International, 1990, 6(4):259-265. doi: 10.1002/(ISSN)1099-1638
|
[8] |
BORGONOVO E.A new uncertainty importance measure.Reliability Engineering & System Safety, 2007, 92(6):771-784. http://paris.utdallas.edu/IJPE/Vol05/Issue03/VOL5N3P2FLGR.pdf
|
[9] |
SALTELLI A, MARIVOET J.Non-parametric statistics in sensitivity analysis for model output:A comparison of selected techniques.Reliability Engineering & System Safety, 1990, 28(2):229-253. https://www.researchgate.net/publication/223733810_Non-parametric_statistics_in_sensitivity_analysis_for_model_output_A_comparison_of_selected_techniques
|
[10] |
SOBOL I M.Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.Mathematics and Computers in Simulation, 2001, 55(1):271-280. https://www.researchgate.net/publication/222535147_Global_sensitivity_indices_for_nonlinear_mathematical_models_and_their_Monte_Carlo_estimates
|
[11] |
NOVI INVERARDI P L, TAGLIANI A.Maximum entropy density estimation from fractional moments.Communications in Statistics-Theory and Methods, 2003, 32(2):327-345. doi: 10.1081/STA-120018189
|
[12] |
DENG J, PANDEY M D.Estimation of the maximum entropy quantile function using fractional probability weighted moments.Structural Safety, 2008, 30(4):307-319. doi: 10.1016/j.strusafe.2007.05.005
|
[13] |
ZHANG X, PANDEY M D.Structural reliability analysis based on the concepts of entropy, fractional moment and dimensional reduction method.Structural Safety, 2013, 43(9):28-40. https://www.researchgate.net/publication/257007041_Structural_reliability_analysis_based_on_the_concepts_of_entropy_fractional_moment_and_dimensional_reduction_method
|
[14] |
CREMERS D, OSHER S J, SOATTO S.Kernel density estimation and intrinsic alignment for shape priors in level set segmentation.International Journal of Computer Vision, 2015, 69(3):335-351. doi: 10.1007/s11263-006-7533-5
|
[15] |
张磊刚, 吕震宙, 陈军.基于失效概率的矩独立重要性测度的高效算法.航空学报, 2014, 35(8):2199-2206. http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201408013.htm
ZHANG L G, LYU Z Z, CHEN J.An efficient method for failure probability-based moment-independent importance measure.Acta Aeronautica et Astronautica Sinica, 2014, 35(8):2199-2206(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201408013.htm
|
[16] |
LEIBLER R A, KULLBACK S.On information and sufficiency.Annals of Mathematical Statistics, 1951, 22(1):79-86. doi: 10.1214/aoms/1177729694
|
[17] |
尹晓伟, 钱文学, 谢里阳.系统可靠性的贝叶斯网络评估方法.航空学报, 2008, 29(6):1482-1489. http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200806013.htm
YIN X W, QIAN W X, XIE L Y.A method for system reliability assessment based on bayesian networks.Acta Aeronautica et Astronautica Sinica, 2008, 29(6):1482-1489(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200806013.htm
|
[18] |
袁朝辉, 崔华阳, 侯晨光.民用飞机电液舵机故障树分析.机床与液压, 2006(11):221-223. doi: 10.3969/j.issn.1001-3881.2006.11.075
YUAN C H, CUI H Y, HOU C G.Fault tree analysis of civil aircraft electro-hydraulic actuator.Machine Tool & Hydraulics, 2006(11):221-223(in Chinese). doi: 10.3969/j.issn.1001-3881.2006.11.075
|