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In the production and operation, inherent variability and uncertainty necessitate addressing unit-to-unit heterogeneity in initial performance values and degradation processes. This article presents a bi-stochastic exponential dispersion process (BS-ED) designed to account for heterogeneity in both initial performance values and degradation processes. First, based on the ED process, the time and acceleration covariates are introduced to form a nonlinear accelerated ED process, and a random effect coefficient associated with the accelerated stress is incorporated to consider the heterogeneity of the process. Meanwhile, through the modelling of degradation time-shift, a degradation model considering the stochastic initial value of the product performance is developed. To effectively conduct the statistic inference of the BS-ED process, an improved stochastic EM algorithm is proposed, and the information matrix and Ito calculus are combined to estimate the confidence intervals. Finally, the stability of the method is verified by simulation and analyzed by two real cases.
Czasopismo
Rocznik
Tom
Strony
art. no 191432
Opis fizyczny
Bibliogr. 37 poz., rys., tab., wykr.
Twórcy
autor
- School of Safety Science and Engineering, Civil Aviation University of China, China
autor
- Institute of Science and Technology Innovation, Civil Aviation University of China, China
- Key Laboratory of Civil Aviation Aircraft Airworthiness Certification Technology, Civil Aviation University of China, China
autor
- School of Safety Science and Engineering, Civil Aviation University of China, China
autor
- Institute of Science and Technology Innovation, Civil Aviation University of China, China
Bibliografia
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- 11. Yan W, Xu X, Bigaud D, Cao W. Optimal design of step-stress accelerated degradation tests based on the Tweedie exponential dispersion process. Reliability Engineering & System Safety 2023;230:108917. https://doi.org/10.1016/j.ress.2022.108917.
- 12. Yan W, Riahi H, Benzarti K, Chlela R, Curtil L, Bigaud D. Durability and Reliability Estimation of Flax Fiber Reinforced Composites Using Tweedie Exponential Dispersion Degradation Process. Mathematical Problems in Engineering 2021;2021:1–21. https://doi.org/10.1155/2021/6629637.
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- 14. Wang Y, Zhang C, Zhang S, Chen X, Tan Y. Optimal design of constant stress accelerated degradation test plan with multiple stresses and multiple degradation measures. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2015;229:83–93. https://doi.org/10.1177/1748006X14552312.
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- 16. Limon S, Rezaei E, Yadav OP. Designing an accelerated degradation test plan considering the gamma degradation process with multi-stress factors and interaction effects. Quality Technology & Quantitative Management 2020;17:544–60. https://doi.org/10.1080/16843703.2019.1696010.
- 17. Wang H, Xu T, Mi Q. Lifetime prediction based on Gamma processes from accelerated degradation data. Chinese Journal of Aeronautics 2015;28:172–9. https://doi.org/10.1016/j.cja.2014.12.015.
- 18. Ma Z, Nie S, Liao H. A load spectra design method for multi-stress accelerated testing. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2022;236:994–1006. https://doi.org/10.1177/1748006X211062012.
- 19. He D, Wang Y, Chang G. Objective Bayesian analysis for the accelerated degradation model based on the inverse Gaussian process. Applied Mathematical Modelling 2018;61:341–50. https://doi.org/10.1016/j.apm.2018.04.025.
- 20. Shen L, Wang Y, Zhai Q, Tang Y. Degradation Modeling Using Stochastic Processes With Random Initial Degradation. IEEE Trans Rel 2019;68:1320–29. https://doi.org/10.1109/TR.2018.2885133.
- 21. Zheng B, Chen C, Lin Y, Ye X, Zhai G. Reliability Analysis Based on a Bivariate Degradation Model Considering Random Initial State and Its Correlation With Degradation Rate. IEEE Trans Rel 2023;72:37–48. https://doi.org/10.1109/TR.2022.3172416
- 22. Rodríguez-Picón LA, Méndez-González LC, Flores-Ochoa VH, Pérez Olguín IJ, García V. Degradation modeling based on the gamma process with random initial degradation level and random threshold. Quality Technology & Quantitative Management 2023:1–21. https://doi.org/10.1080/16843703.2022.2146904.
- 23. Xiao X, Ye Z. Optimal Design for Destructive Degradation Tests With Random Initial Degradation Values Using the Wiener Process. IEEE Trans Rel 2016;65:1327–42. https://doi.org/10.1109/TR.2016.2575442.
- 24. Yan B, Wang H, Ma X. Modeling left-truncated degradation data using random drift-diffusion Wiener processes. Quality Technology & Quantitative Management 2023:1–24. https://doi.org/10.1080/16843703.2023.2187011.
- 25. Hou Y, Du Y, Peng Y, Liu D. An Improved Random Effects Wiener Process Accelerated Degradation Test Model for Lithium-Ion Battery. IEEE Trans Instrum Meas 2021;70:1–11. https://doi.org/10.1109/TIM.2021.3091457.
- 26. Tang S, Wang F, Sun X, Xu X, Yu C, Si X. Unbiased parameters estimation and mis-specification analysis of Wiener process-based degradation model with random effects. Applied Mathematical Modelling 2022;109:134–60. https://doi.org/10.1016/j.apm.2022.03.039.
- 27. Sun F, Fu F, Liao H, Xu D. Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula. Reliability Engineering & System Safety 2020;204:107168. https://doi.org/10.1016/j.ress.2020.107168.
- 28. Xiao M, Zhang Y, Li Y, Wang W. Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity. IEEE Access 2020;8:160982–94. https://doi.org/10.1109/ACCESS.2020.3020723.
- 29. Wang Z, Zhai Q, Chen P. Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study. Reliability Engineering & System Safety 2021;216:107897. https://doi.org/10.1016/j.ress.2021.107897.
- 30. Ye Z-S, Chen L-P, Tang LC, Xie M. Accelerated Degradation Test Planning Using the Inverse Gaussian Process. IEEE Trans Rel 2014;63:750–63. https://doi.org/10.1109/TR.2014.2315773.
- 31. Hao S, Yang J, Berenguer C. Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors. Reliability Engineering & System Safety 2019;189:261–70. https://doi.org/10.1016/j.ress.2019.04.031.
- 32. Sun B, Li Y, Wang Z, Ren Y, Feng Q, Yang D. An improved inverse Gaussian process with random effects and measurement errors for RUL prediction of hydraulic piston pump. Measurement 2021;173:108604. https://doi.org/10.1016/j.measurement.2020.108604.
- 33. Liu D, Wang S, Zhang C, Tomovic M. Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process. Reliability Engineering & System Safety 2018;180:25–38. https://doi.org/10.1016/j.ress.2018.06.019.
- 34. Hao H, Su C, Li C. LED Lighting System Reliability Modeling and Inference via Random Effects Gamma Process and Copula Function. International Journal of Photoenergy 2015;2015:1–8. https://doi.org/10.1155/2015/243648.
- 35. Wang H, Xu T, Mi Q. Lifetime prediction based on Gamma processes from accelerated degradation data. Chinese Journal of Aeronautics 2015;28:172–9. https://doi.org/10.1016/j.cja.2014.12.015.
- 36. W. Meeker and L. Escobar, Statistical Methods for Reliability Data. New York, NY, USA: Wiley, 1998.
- 37. Burnham KP, Anderson DR. Multi-model Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research 2004;33:261–304. https://doi.org/10.1177/0049124104268644.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f888a77e-3f6f-4c65-a9d9-68e7a0fbf87b
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