Multioutput Extreme Spatial Model for Complex Aircraft Production Systems

References

• AlBahar A, Kim I, Wang X, Yue X (2022) Physics-constrained Bayesian optimization for optimal actuators placement in composite structures assembly. IEEE Trans. Automation Sci. Engrg. 20(4):2772–2783.Crossref, Google Scholar
• Ankenman BE, Nelson BL, Staum J (2010) Stochastic kriging for simulation metamodeling. Oper. Res. 58(2):371–382.Link, Google Scholar
• Bacro JN, Gaetan C, Opitz T, Toulemonde G (2020) Hierarchical space-time modeling of asymptotically independent exceedances with an application to precipitation data. J. Amer. Statist. Assoc. 115(530):555–569.Crossref, Google Scholar
• Bonilla EV, Chai K, Williams C (2007) Multi-task Gaussian process prediction. Platt J, Koller D, Singer Y, Roweis S, eds. Adv. Neural Inform. Processing Systems, vol. 20 (Curran Associates, Inc., Red Hook, NY).Google Scholar
• Bopp GP, Shaby BA, Huser R (2021) A hierarchical max-infinitely divisible spatial model for extreme precipitation. J. Amer. Statist. Assoc. 116(533):93–106.Crossref, Google Scholar
• Boyce BL, Salzbrenner BC, Rodelas JM, Swiler LP, Madison JD, Jared BH, Shen YL (2017) Extreme-value statistics reveal rare failure-critical defects in additive manufacturing. Adv. Engrg. Materials 19(8):1700102.Crossref, Google Scholar
• Brown BM, Resnick SI (1977) Extreme values of independent stochastic processes. J. Appl. Probab. 14(4):732–739.Crossref, Google Scholar
• Castruccio S, Huser R, Genton MG (2016) High-order composite likelihood inference for max-stable distributions and processes. J. Comput. Graphical Statist. 25(4):1212–1229.Crossref, Google Scholar
• Cho W, Kim Y, Park J (2020) Hierarchical anomaly detection using a multioutput Gaussian process. IEEE Trans. Automation Sci. Engrg. 17(1):261–272.Crossref, Google Scholar
• Coles S, Bawa J, Trenner L, Dorazio P (2001) An Introduction to Statistical Modeling of Extreme Values, vol. 208 (Springer, New York).Crossref, Google Scholar
• Cooley D, Naveau P, Poncet P (2006) Variograms for spatial max-stable random fields. Bertail P, Soulier P, Doukhan P, eds. Lecture Notes in Statistics, vol. 187 (Springer, New York), 373–390.Google Scholar
• Dahan E, Mendelson H (2001) An extreme-value model of concept testing. Management Sci. 47(1):102–116.Link, Google Scholar
• Davison AC, Padoan SA, Ribatet M (2012) Statistical modeling of spatial extremes. Statist. Sci. 27(2):161–186.Crossref, Google Scholar
• de Haan L (1984) A spectral representation for max-stable processes. Ann. Probab. 12(4):1194–1204.Google Scholar
• de Haan L, Ferreira A (2007) Extreme Value Theory: An Introduction (Springer, New York).Google Scholar
• Devalkar SK, Anupindi R, Sinha A (2018) Dynamic risk management of commodity operations: Model and analysis. Manufacturing Service Oper. Management 20(2):317–332.Link, Google Scholar
• Dombry C, Engelke S, Oesting M (2016) Exact simulation of max-stable processes. Biometrika 103(2):303–317.Crossref, Google Scholar
• Engelke S, Hitz AS (2020) Graphical models for extremes. J. Roy. Statist. Soc. Ser. B Statist. Methodology 82(4):871–932.Crossref, Google Scholar
• Grigoriu M (2017) Estimates of system response maxima by extreme value theory and surrogate models. SIAM/ASA J. Uncertainty Quantification 5(1):922–955.Crossref, Google Scholar
• Gu T, Stopka KS, Xu C, McDowell DL (2020) Prediction of maximum fatigue indicator parameters for duplex Ti–6Al–4V using extreme value theory. Acta Materialia 188:504–516.Crossref, Google Scholar
• He S, Zhang Z, Jiang W, Bian D (2018) Predicting field reliability based on two-dimensional warranty data with learning effects. J. Quality Tech. 50(2):198–206.Crossref, Google Scholar
• Huser R, Wadsworth JL (2019) Modeling spatial processes with unknown extremal dependence class. J. Amer. Statist. Assoc. 114(525):434–444.Crossref, Google Scholar
• Huser R, Wadsworth JL (2020) Advances in statistical modeling of spatial extremes. WIREs Comput. Statist. 14(1):e1537.Crossref, Google Scholar
• King CB, Hong Y, Dehart SP, Defeo PA, Pan R (2016) Planning fatigue tests for polymer composites. J. Quality Tech. 48(3):227–245.Crossref, Google Scholar
• Lee C, Wang X, Wu J, Yue X (2022a) Failure-averse active learning for physics-constrained systems. IEEE Trans. Automation Sci. Engrg. 20(4):2215–2226.Google Scholar
• Lee C, Wu J, Wang W, Yue X (2022b) Neural network Gaussian process considering input uncertainty for composite structure assembly. IEEE/ASME Trans. Mechatronics 27(3):1267–1277.Crossref, Google Scholar
• Lee C, Wang K, Wu J, Cai W, Yue X (2023) Partitioned active learning for heterogeneous systems. J. Comput. Inform. Sci. Engrg. 23(4):041009.Crossref, Google Scholar
• Lewis-Beck C, Tian Q, Meeker WQ (2022) Prediction of future failures for heterogeneous reliability field data. Technometrics 64(1):125–138.Crossref, Google Scholar
• Liu S, Meeker WQ (2015) Statistical methods for estimating the minimum thickness along a pipeline. Technometrics 57(2):164–179.Crossref, Google Scholar
• Montgomery DC (2020) Introduction to Statistical Quality Control (John Wiley & Sons, New York).Google Scholar
• Opitz T (2013) Extremal t processes: Elliptical domain of attraction and a spectral representation. J. Multivariate Anal. 122:409–413.Crossref, Google Scholar
• Plumlee M, Tuo R (2014) Building accurate emulators for stochastic simulations via quantile kriging. Technometrics 56(4):466–473.Crossref, Google Scholar
• Resnick SI (2013) Extreme Values, Regular Variation and Point Processes (Springer, London).Google Scholar
• Sang H, Gelfand AE (2010) Continuous spatial process models for spatial extreme values. J. Agricultural Biol. Environ. Statist. 15(1):49–65.Crossref, Google Scholar
• Santner TJ, Williams BJ, Notz WI (2018) The Design and Analysis of Computer Experiments, vol. 2 (Springer, New York).Crossref, Google Scholar
• Schmidt W, Raman A (2022) Operational disruptions, firm risk, and control systems. Manufacturing Service Oper. Management 24(1):411–429.Link, Google Scholar
• Shi J (2006) Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes (CRC Press, Boca Raton, FL).Crossref, Google Scholar
• Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, et al. (2020) SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods 17(3):261–272.Crossref, Google Scholar
• Wen Y, Yue X, Hunt JH, Shi J (2019) Virtual assembly and residual stress analysis for the composite fuselage assembly process. J. Manufacturing Systems 52:55–62.Crossref, Google Scholar
• Xia T, Fang X, Gebraeel N, Xi L, Pan E (2019) Online analytics framework of sensor-driven prognosis and opportunistic maintenance for mass customization. J. Manufacturing Sci. Engrg. 141(5):051011.Crossref, Google Scholar
• Yu S, Wang Z, Meng D (2018) Time-variant reliability assessment for multiple failure modes and temporal parameters. Structural Multidisciplinary Optim. 58(4):1705–1717.Crossref, Google Scholar
• Yu X, Zhao Z, Zhang X, Zhang Q, Liu Y, Sun C, Chen X (2022) Deep-learning-based open set fault diagnosis by extreme value theory. IEEE Trans. Indust. Inform. 18(1):185–196.Crossref, Google Scholar
• Yue X, Wen Y, Hunt JH, Shi J (2018) Surrogate model-based control considering uncertainties for composite fuselage assembly. J. Manufacturing Sci. Engrg. 140(4):041017.Google Scholar
• Zhan Z, Xu M, Xu S (2015) Predicting cyber attack rates with extreme values. IEEE Trans. Inform. Forensics Security 10(8):1666–1677.Crossref, Google Scholar