Inverse probabilistic modelling of the sources of uncertainty: a non-parametric simulated-likelihood method with application to an industrial turbine vibration assessment

References

• Parry, GW, and Winter, PW, 1981. Characterization and evaluation of uncertainty in probabilistic risk analysis, Nucl. Safety 22 (1981), pp. 28–42.
   Google Scholar
• Beck, MB, 1987. Water quality modelling: A review of the analysis of uncertainty, Wat. Res. 23 (1987), pp. 1393–1442.
   Web of Science ®Google Scholar
• Apostolakis, G, 1990. The concept of probability in safety assessments of technological systems, Science 250 (1990), pp. 1359–1364.
   PubMed Web of Science ®Google Scholar
• Granger Morgan, M, and Henrion, M, 1990. Uncertainty–A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. London: Cambridge University Press; 1990.
   Google Scholar
• Helton, JC, and Burmaster, DE, 1996. Guest editorial: Treatment of aleatory and epistemic uncertainty in performance assessments for complex systems, Rel. Eng. Syst. Saf. 54 (1996), pp. 91–94.
   Web of Science ®Google Scholar
• Beck, JV, and Arnold, KJ, 1977. Parameter Estimation in Engineering and Science. New York: Wiley; 1977.
   Google Scholar
• Tarantola, A, 2004. Tarantola, A, ed. Inverse problem theory and methods for data fitting and model parameter estimation. Amsterdam: SIAM, Elsevier; 2004, 1987.
   Google Scholar
• Talagrand, O, and Courtier, P, 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation, I: Theory, Q. J. R. Meteorol. Soc. 113 (1987), pp. 1311–1328.
   Web of Science ®Google Scholar
• Helton, J, 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal, Rel. Eng. Syst. Saf. 42 (1993), pp. 327–367.
   Web of Science ®Google Scholar
• Paté-Cornell, ME, 1996. Uncertainties in risk analysis: Six levels of treatment, Reliab. Eng. Syst. Saf. 54 (1996), pp. 95–111.
   Web of Science ®Google Scholar
• Helton, JC, and Oberkampf, WL, 2004. Alternative representations of epistemic uncertainty, Rel. Eng. Syst. Saf. 85 (1–3) (2004), pp. 1–369.
   Web of Science ®Google Scholar
• Talagrand, O, 1997. Assimilation of observations, an introduction, J. Meteor. Soc. Japan 75 (1997), pp. 191–201.
   Web of Science ®Google Scholar
• Walter, E, and Pronzato, L, 1994. Identification de Modèles Paramétriques à partir de données expérimentales. Masson: Coll. MASC; 1994.
   Google Scholar
• Beck, JL, and Katafygiotis, LS, 1998. Updating models and their uncertainties, J. Eng. Mech. I: Bayesian Statistical Framework 124 (1998), pp. 455–461.
   Google Scholar
• Casarotti, C, Petrini, L, and Strobbia, C, 2007. "Calibration of non-linear model parameters via inversion of experimental data and propagation of uncertainties in seismic fibre element analysis". In: Proceedings of Computational Methods in Structural Dynamics and Earthquake Engineering, Rethymno. 2007.
   Google Scholar
• Mahé, P, and Rocquigny, E de, 2005. Incertitudes non observables en calcul scientifique industriel–Etude d’algorithmes simples pour intégrer dispersion intrinsèque et ébauche d’expert, 37èmes J. Franc. Stat. Pau (2005), France.
   Google Scholar
• Du, C, Kurowicka, D, and Cooke, RM, 2006. Techniques for generic probabalistic inversion, Computational Statistics & Data Analysis 50 (5) (2006), pp. 1164–1187.
   Web of Science ®Google Scholar
• De Crécy, A, 1997. CIRCE: A tool for calculating the uncertainties of the constitutive relationships of Cathare2, in 8th International Topical Meeting on Nuclear reactor Thermo-Hydraulics (NURETH8). Kyoto. 1997.
   Google Scholar
• Dempster, F, Laird, NM, and Rubin, DB, 1977. Maximum likelihood from incomplete data from the EM algorithm, J. Roy. Stat. Soc. (Ser. B) 39 (1977), pp. 1–38.
   Google Scholar
• de Rocquigny, E, 2006. La maîtrise des incertitudes dans un contexte industriel: 1ère partie–Une approche méthodologique globale basée sur des exemples; 2nd partie–Revue des méthodes de modélisation statistique, physique et numérique, J. Soc. Française Stat. 147 (2006), pp. 33–106.
   Google Scholar
• Rocquigny, E de, Devictor, N, and Tarantola, S, 2008. Uncertainty in Industrial Practice, A Guide To Quantitative Uncertainty Management. Chichester: Wiley; 2008.
   Google Scholar
• Ionescu-Bujor, M, and Cacuci, DG, 2004. A comparative review of sensitivity and uncertainty analysis of large-scale systems–I: Deterministic methods, Nucl. Sci. Eng. 147 (2004), pp. 189–2003.
   Web of Science ®Google Scholar
• Cacuci, DG, and Ionescu-Bujor, M, 2004. A comparative review of sensitivity and uncertainty analysis of large-scale systems–II: Statistical methods, Nucl. Sci. Eng. 147 (2004), pp. 204–217.
   Web of Science ®Google Scholar
• Hamby, DM, 1994. A review of techniques for parameter sensitivity analysis of environmental models, Environ. Monit. Assess. 32 (1994), pp. 135–154.
   PubMed Web of Science ®Google Scholar
• Frey, HC, and Patil, SR, 2002. Identification and review of sensitivity analysis methods, Risk Anal. 22 (2002), pp. 553–578.
   PubMed Web of Science ®Google Scholar
• Saltelli, A, Tarantola, S, Campolongo, F, and Ratto, M, 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Chichester: John Wiley and Sons; 2004.
   Google Scholar
• Helton, JC, Johnson, JD, Sallaberry, CJ, and Storlie, CB, 2006. Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliab. Eng. Syst. Saf. 91 (2006), pp. 1175–1209.
   Web of Science ®Google Scholar
• Soize, C, 2005. Random matrix theory for modelling uncertainties in computational mechanics, Comput. Methods Appl. Mech. Eng. 194 (2005), pp. 1333–1366.
   Web of Science ®Google Scholar
• Silverman, BW, 1986. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall; 1986.
   Google Scholar
• Rubinstein, RY, 1981. Simulation and the Monte-Carlo method. New York: Wiley; 1981.
   Google Scholar
• Cramér, H, 1946. Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press; 1946.
   Google Scholar
• Jennrich, RI, 1969. Asymptotic properties of non-linear least squares estimators, Ann. Math. Statist. 40 (1969), pp. 633–643.
   Google Scholar
• Hoadley, B, 1971. Asymptotic properties of maximum likelihood estimators for the independent not identically distributed case, Ann. Math. Statist. 42 (1971), pp. 1977–1991.
   Google Scholar
• Ratier, L, 2004. "Modelling a mistuned bladed disk by modal reduction". In: Proceedings of ISMA2004, Leuven 2004. 2004.
   Google Scholar
• Soize, C, 2000. A non-parametric model of random uncertainties for reduced matrix models in structural dynamics, Probab. Eng. Mech. 15 (2000), pp. 277–294.
   Web of Science ®Google Scholar
• Capiez-Lernout, E, and Soize, C, 2004. Nonparametric modeling of random uncertainties for dynamic response of mistuned bladed disks, J. Eng. Gas Turbine Power 126 (2004), pp. 610–618.
   Web of Science ®Google Scholar
• Ratier, L, and Voinis, Ph.S, 2002. "Study of blade dynamic stresses with a non intrusive measurement technique: Experimental results on a power station turbine". In: Proceedings of 9th ISROMAC. Honolulu, 2002 February 10–14. 2002.
   Google Scholar