방법 비교

	Degradation Models	정비 최적화	통계적 신뢰성 분석	Weibull 모수 생존 회귀분석
분야	신뢰성	신뢰성	신뢰성	생존분석
계열	Regression model	Process / pipeline	Regression model	Survival analysis
기원 연도	1998	2002	1998	1951
창시자	Meeker, Escobar & Lu	Hongzhou Wang	William Meeker & Luis Escobar	Waloddi Weibull
유형	Stochastic degradation path model	decision optimization framework	Parametric lifetime modeling	Fully parametric survival regression model
원전	Meeker, W. Q., Escobar, L. A., & Lu, C. J. (1998). Accelerated degradation tests: modeling and analysis. Technometrics, 40(2), 89–99.	Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469–489.	Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley. ISBN: 978-0-471-14328-4	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
별칭	Accelerated Degradation Testing, Degradation Path Models, Performance Degradation Analysis, Bozunma Modelleri	Optimal Maintenance Policy, Preventive Maintenance Scheduling, Predictive Maintenance Optimization, Bakım Optimizasyonu	Life Data Analysis, Survival Analysis (Engineering), Time-to-Failure Analysis, Güvenilirlik Analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
관련	3	3	3	4
요약	Degradation models estimate product lifetime by tracking measurable performance characteristics—such as crack length, light output, or insulation resistance—over time rather than waiting for outright failure. Introduced in rigorous form by Meeker, Escobar, and Lu (1998), these models fit a stochastic degradation path to repeated measurements and define failure as the first time the characteristic crosses a predetermined threshold, enabling reliable lifetime inference from accelerated test data with very few or no observed failures.	Maintenance Optimization is a quantitative framework for determining the timing, type, and frequency of maintenance actions—preventive, predictive, or corrective—that minimize total cost or expected downtime over a system's operational life. Systematic formulations were consolidated by Hongzhou Wang (2002), whose survey unified age-replacement, block-replacement, and imperfect-repair policies under a common cost-rate structure applicable to deteriorating systems across engineering and operations management.	Statistical reliability analysis models the time-to-failure of components, systems, or products using parametric lifetime distributions fitted to observed or censored failure data. Formalized comprehensively by William Q. Meeker and Luis A. Escobar in their 1998 Wiley monograph, the framework integrates maximum likelihood estimation, censoring mechanisms, and distributional diagnostics to produce probability-of-failure curves, hazard rates, and quantile estimates that support design, warranty, and maintenance decisions.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.