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	Modelli di degradazione	Analisi statistica di affidabilità	Regressione di Sopravvivenza Parametrica di Weibull
Campo	Affidabilità	Affidabilità	Analisi di sopravvivenza
Famiglia	Regression model	Regression model	Survival analysis
Anno di origine	1998	1998	1951
Ideatore	Meeker, Escobar & Lu	William Meeker & Luis Escobar	Waloddi Weibull
Tipo	Stochastic degradation path model	Parametric lifetime modeling	Fully parametric survival regression model
Fonte seminale	Meeker, W. Q., Escobar, L. A., & Lu, C. J. (1998). Accelerated degradation tests: modeling and analysis. Technometrics, 40(2), 89–99.	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.
Alias	Accelerated Degradation Testing, Degradation Path Models, Performance Degradation Analysis, Bozunma Modelleri	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
Correlati	3	3	4
Sintesi	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.	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.