Linganisha mbinu

	Uboreshaji wa Matengenezo	Regressioni ya Kuishi ya Weibull ya Parametric
Nyanja	Utegemewa	Uchanganuzi wa Uhai
Familia	Process / pipeline	Survival analysis
Mwaka wa asili	2002	1951
Mwanzilishi	Hongzhou Wang	Waloddi Weibull
Aina	decision optimization framework	Fully parametric survival regression model
Chanzo asilia	Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469–489.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Majina mbadala	Optimal Maintenance Policy, Preventive Maintenance Scheduling, Predictive Maintenance Optimization, Bakım Optimizasyonu	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Zinazohusiana	3	4
Muhtasari	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.	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.