Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Ocena niezawodności z wykorzystaniem stochastycznego modelu wzrostu temperatury w metalowych wytłoczkach, uwzględniającego wielorakie zmienne naprężeniowe oraz efekty losowe
Języki publikacji
Abstrakty
Many products wear out over time even before they fail or stop working, therefore, through accelerated degradation tests one is able to make inferences about statistical parameters or the distributions of a product useful life. Since many devices experience different types of variation due to unobservable factors during the manufacturing processes or under certain operating conditions; these situations lead to the need in developing accelerated degradation models with several variables of acceleration and random effects. The proposed model in this paper, is a model based on the gamma process with random effects to have a better analysis of degradation. This model is applied to the analysis of the temperature increase of metal stampings that are affected by multiple explanatory variables. In addition, a statistical inference method based on a Bayesian approach is used to estimate the unknown parameters to then perform a reliability analysis after obtaining the first-passage time distributions.
Wiele produktów zużywa się z upływem czasu zanim nawet ulegną uszkodzeniu lub przestaną działać. Badania przyspieszonego starzenia pozwalają wyciągać wnioski na temat parametrów statystycznych lub rozkładów okresu użytkowania produktu. Wiele urządzeń podlega różnym rodzajom zmienności pod wpływem działania nieobserwowalnych czynników występujących podczas procesu produkcyjnego lub w pewnych warunkach pracy; sytuacje te wymagają opracowania modeli przyspieszonego starzenia uwzględniających wielorakie zmienne przyspieszenia oraz efekty losowe. Zaproponowany w przedstawionym artykule model opiera się na procesie gamma z efektami losowymi, dzięki czemu pozwala na lepszą analizę degradacji. Model ten zastosowano do analizy wzrostu temperatury w metalowych wytłoczkach, na które oddziałuje wiele zmiennych objaśniających. Ponadto do oszacowania nieznanych parametrów wykorzystano metodę wnioskowania statystycznego opartą na podejściu bayesowskim. Umożliwiło to analizę niezawodności po uzyskaniu rozkładów czasu pierwszego przejścia.
Czasopismo
Rocznik
Tom
Strony
654--661
Opis fizyczny
Bibliogr. 50 poz., rys., tab.
Twórcy
- Department Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Av. Plutarco Elías Calles 1210, Fovissste Chamizal 32310 Ciudad Juárez, Chihuahua, México
- Department Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Av. Plutarco Elías Calles 1210, Fovissste Chamizal 32310 Ciudad Juárez, Chihuahua, México
autor
- Department Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Av. Plutarco Elías Calles 1210, Fovissste Chamizal 32310 Ciudad Juárez, Chihuahua, México
- Department Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Av. Plutarco Elías Calles 1210, Fovissste Chamizal 32310 Ciudad Juárez, Chihuahua, México
Bibliografia
- 1. Akaike H. Factor analysis and AIC. In: Parzen E, Tanabe K, Kitagawa G, editors. Selected Papers of Hirotugu Akaike. New York, NY:Springer New York 1998; 371-386, https://doi.org/10.1007/978-1-4612-1694-0_29.
- 2. Andrzejczak K, Młyńczak M, Selech J. Poisson-distributed failures in the predicting of the cost of corrective maintenance. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 602-609, https://doi.org/10.17531/ein.2018.4.11.
- 3. Barker CT, Newby MJ. Optimal non-periodic inspection for a multivariate degradation model. Reliability Engineering & System Safety 2009; 94(1): 33-43, https://doi.org/10.1016/j.ress.2007.03.015.
- 4. Bordes L, Paroissin C, Salami A. Parametric inference in a perturbed gamma degradation process. Communications in Statistics - Theory and Methods 2016; 45(9): 2730-2747, https://doi.org/10.1080/03610926.2014.892133.
- 5. Cheng Y-S, Peng C-Y. Integrated Degradation Models in R Using iDEMO. Journal of Statistical Software 2012; 49(2): 22, https://doi.org/10.18637/jss.v049.i02.
- 6. Cox DR. Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B (Methodological) 1972; 34(2): 187-202, https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.
- 7. Crowder M, Lawless J. On a scheme for predictive maintenance. European Journal of Operational Research 2007; 176(3): 1713-1722, https://doi.org/10.1016/j.ejor.2005.10.051.
- 8. deLeeuw J. Introduction to Akaike (1973) Information Theory and an Extension of the Maximum Likelihood Principle. In: Kotz S, Johnson NL, editors. Breakthroughs in Statistics: Foundations and Basic Theory. New York, NY: Springer New York 1992; 599-609, https://doi.org/10.1007/978-1-4612-0919-5_37.
- 9. Doksum KA, Normand S-LT. Gaussian models for degradation processes-part I: Methods for the analysis of biomarker data. Lifetime Data Analysis 1995; 1(2): 131-144, https://doi.org/10.1007/BF00985763.
- 10. Esary JD, Marshall AW. Shock models and wear processes. The Annals of Probability 1973; 1(4): 627-649, https://doi.org/10.1214/aop/1176996891.
- 11. Fan T-H, Wang W-L, Balakrishnan N. Exponential progressive step-stress life-testing with link function based on Box-Cox transformation.Journal of Statistical Planning and Inference 2008; 138(8): 2340-2354, https://doi.org/10.1016/j.jspi.2007.10.002.
- 12. Gelfand AE, Smith AFM. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 1990; 85(410): 398-409, https://doi.org/10.1080/01621459.1990.10476213.
- 13. Hong S, Zhou Z. Application of gaussian process regression for bearing degradation assessment. 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012) 2012; 644-648.
- 14. Iervolino I, Giorgio M, Chioccarelli E. Gamma degradation models for earthquake-resistant structures. Structural Safety 2013; 45: 48-58, https://doi.org/10.1016/j.strusafe.2013.09.001.
- 15. Joseph VR, Yu IT. Reliability improvement experiments with degradation data. IEEE Transactions on Reliability 2006; 55(1): 149-157, https://doi.org/10.1109/TR.2005.858096.
- 16. Lawless J, Crowder M. Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis 2004; 10(3): 213-227, https://doi.org/10.1023/B:LIDA.0000036389.14073.dd.
- 17. Meeker WQ, Escobar LA. Statistical Methods for Reliability Data. New York: JOHN WILEY & SONS, INC 1998.
- 18. Miranda AV. La industria automotriz en México: Antecedentes, situación actual y perspectivas. Contaduría y administración 2007; (221): 1-38.
- 19. Mohanty S, Chattopadhyay A, Peralta P, Das S, Willhauck C. Fatigue Life Prediction Using Multivariate Gaussian Process. 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 16th AIAA/ASME/AHS Adaptive Structures Conference,10th AIAA Non-Deterministic Approaches Conference, 9th AIAA Gossamer Spacecraft Forum, 4th AIAA Multidisciplinary Design Optimization Specialists Conference. Structures, Structural Dynamics, and Materials and Co-located Conferences: American Institute of Aeronautics and Astronautics 2008; 1-14, https://doi.org/10.2514/6.2008-1837.
- 20. Padgett WJ, Durham SD, Mason AM. Weibull Analysis of the Strength of Carbon Fibers Using Linear and Power Law Models for the Length Effect. Journal of Composite Materials 1995; 29(14): 1873-1884, https://doi.org/10.1177/002199839502901405.
- 21. Pan Z, Sun Q, Feng J. Reliability modeling of systems with two dependent degrading components based on gamma processes. Communications in Statistics - Theory and Methods 2016; 45(7): 1923-1938, https://doi.org/10.1080/03610926.2013.870201.
- 22. Pan ZF, Jing; Sun, Quan. Lifetime distribution and associated inference of systems with multiple degradation measurements based on gamma processes. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18 (2): 307-313, https://doi.org/10.17531/ein.2016.2.20.
- 23. Park C, Padgett WJ. Stochastic degradation models with several accelerating variables. IEEE Transactions on Reliability 2006; 55(2): 379-390, https://doi.org/10.1109/TR.2006.874937.
- 24. Park C, Padgett WJ. Cumulative Damage Models for Failure with Several Accelerating Variables. Quality Technology & Quantitative Management 2007; 4(1): 17-34, https://doi.org/10.1080/16843703.2007.11673132.
- 25. Peng C-Y. Inverse Gaussian processes with random effects and explanatory variables for degradation data. Technometrics 2015; 57(1): 100-111, https://doi.org/10.1080/00401706.2013.879077.
- 26. Peng C, Tseng S. Mis-specification analysis of linear degradation models. IEEE Transactions on Reliability 2009; 58(3): 444-455, https://doi.org/10.1109/TR.2009.2026784.
- 27. Peng W, Li Y-F, Yang Y-J, Huang H-Z, Zuo MJ. Inverse Gaussian process models for degradation analysis: A Bayesian perspective. Reliability Engineering & System Safety 2014; 130: 175-189, https://doi.org/10.1016/j.ress.2014.06.005.
- 28. Richter P, Toledano-Ayala M. Revisiting Gaussian process regression modeling for localization in wireless sensor networks. Sensors 2015; 15(9): 22587-22615, https://doi.org/10.3390/s150922587.
- 29. Rodríguez Picón LA, Rodríguez Borbón MI, Flores Ortega A. Estimación de confiabilidad para productos con dos características de desempeño basada en un modelo de degradación estocástico bivariado. CULCyT 2015; (57): 64-75.
- 30. Rodríguez Picón LA, Rodríguez Picón AP, Méndez González LC, Rodríguez Borbón MI, Alvarado Iniesta A. Degradation modeling based on gamma process models with random effects. Communications in Statistics - Simulation and Computation 2018; 47(6): 1796-1810.
- 31. Takeda E, Suzuki N. An empirical model for device degradation due to hot-carrier injection. IEEE Electron Device Letters 1983; 4(4): 111-113, https://doi.org/10.1109/EDL.1983.25667.
- 32. Tseng S-T, Yu H-F. A termination rule for degradation experiments. IEEE Transactions on Reliability 1997; 46(1): 130-133, https://doi.org/10.1109/24.589938.
- 33. Tseng ST, Tang J, Ku IH. Determination of burn‐in parameters and residual life for highly reliable products. Naval Research Logistics 2003;50(1): 1-14, https://doi.org/10.1002/nav.10042.
- 34. Tseng ST, Tsai CC, Balakrishnan N. Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process 2011: 1-18, https://doi.org/10.1002/0471667196.ess7151.
- 35. van Noortwijk J, Kok M, Cooke R. Optimal maintenance decisions for the sea-bed protection of the Eastern-Scheldt barrier. In: Cooke R, Mendel M, Vrijling H, editors. Engineering Probabilistic Design and Maintenance for Flood Protection. Springer, Boston, MA 1997: 25-56, https://doi.org/10.1007/978-1-4613-3397-5_2.
- 36. van Noortwijk JM. A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety 2009; 94(1):2-21, https://doi.org/10.1016/j.ress.2007.03.019.
- 37. van Noortwijk JM, Cooke RM, Kok M. A Bayesian failure model based on isotropic deterioration. European Journal of Operational Research 1995; 82(2): 270-282, https://doi.org/10.1016/0377-2217(94)00263-C.
- 38. Wang H, Xu T, Mi Q. Lifetime prediction based on Gamma processes from accelerated degradation data. Chinese Journal of Aeronautics 2015; 28(1): 172-179. https://doi.org/10.1016/j.cja.2014.12.015
- 39. Wang X. A pseudo-likelihood estimation method for nonhomogeneous gamma process model with random effects. Statistica Sinica 2008;18(3): 1153-1163.
- 40. Wang X. Wiener processes with random effects for degradation data. Journal of Multivariate Analysis 2010; 101(2): 340-351, https://doi.org/10.1016/j.jmva.2008.12.007.
- 41. Wang X, Balakrishnan N, Guo B, Jiang P. Residual life estimation based on bivariate non-stationary gamma degradation process. Journal of Statistical Computation and Simulation 2015; 85(2): 405-421, https://doi.org/10.1080/00949655.2013.824448.
- 42. Wang X, Guo B, Cheng Z. Residual life estimation based on bivariate Wiener degradation process with time-scale transformations. Journal of Statistical Computation and Simulation 2014; 84(3): 545-563, https://doi.org/10.1080/00949655.2012.719026.
- 43. Wang X, Xu D. An inverse Gaussian process model for degradation data. Technometrics 2010; 52(2): 188-197, https://doi.org/10.1198/TECH.2009.08197.
- 44. Wolstenholme LC. A nonparametric test of the weakest-link principle. Technometrics 1995; 37(2): 169-175, https://doi.org/10.1080/00401706.1995.10484301.
- 45. Ye Z-S, Chen L-P, Tang LC, Xie M. Accelerated degradation test planning using the inverse gaussian process. IEEE Transactions on Reliability 2014; 63(3): 750-763, https://doi.org/10.1109/TR.2014.2315773.
- 46. Ye Z-S, Chen N. The inverse Gaussian process as a degradation model. Technometrics 2014; 56(3): 302-311, https://doi.org/10.1080/00401706.2013.830074.
- 47. Ye Z-S, Xie M, Tang L-C, Chen N. Semiparametric estimation of gamma processes for deteriorating products. Technometrics 2014; 56(4):504-513, https://doi.org/10.1080/00401706.2013.869261.
- 48. Ye Z-S, Xie M, Tang L-C, Shen Y. Degradation-based burn-in planning under competing risks. Technometrics 2012; 54(2): 159-168, https://doi.org/10.1080/00401706.2012.676946.
- 49. Zeng D, Lin DY, Yin G. Maximum likelihood estimation for the proportional odds model with random effects. Journal of the American Statistical Association 2005; 100(470): 470-483, https://doi.org/10.1198/016214504000001420.
- 50. Zhang S, Zhou W, Qin H. Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data. Corrosion Science 2013; 73: 309-320, https://doi.org/10.1016/j.corsci.2013.04.020.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5fc1a59c-ff27-4d1e-a763-cef6d99f38e4