Comparar métodos

	Optimización del Mantenimiento	Modelos de degradación	Programación Dinámica	Análisis Estadístico de Fiabilidad
Campo	Fiabilidad	Fiabilidad	Optimización	Fiabilidad
Familia	Process / pipeline	Regression model	Process / pipeline	Regression model
Año de origen	2002	1998	1957	1998
Autor original	Hongzhou Wang	Meeker, Escobar & Lu	Richard Bellman	William Meeker & Luis Escobar
Tipo	decision optimization framework	Stochastic degradation path model	Exact combinatorial optimization via recursive decomposition	Parametric lifetime modeling
Fuente seminal	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., & Lu, C. J. (1998). Accelerated degradation tests: modeling and analysis. Technometrics, 40(2), 89–99.	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6	Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley. ISBN: 978-0-471-14328-4
Alias	Optimal Maintenance Policy, Preventive Maintenance Scheduling, Predictive Maintenance Optimization, Bakım Optimizasyonu	Accelerated Degradation Testing, Degradation Path Models, Performance Degradation Analysis, Bozunma Modelleri	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama	Life Data Analysis, Survival Analysis (Engineering), Time-to-Failure Analysis, Güvenilirlik Analizi
Relacionados	3	3	3	3
Resumen	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.	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.	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.	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.