Bernburg, Hugalf; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Elster, Clemens; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Klauenberg, Katy; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Quelle/Jahr
Applied stochastic models in Business and Industry: 40
(2024), 4, 996
- 1011
ISSN
1526-4025 (online) ; 1524-1904 (print)
Verlag
Chichester: Wiley
Freie Schlagworte
acceptance sampling ; hidden Markov model ; lifetime model ; piecewise homogeneous poisson process ; reliability prediction
Zusammenfassung
he aim is to demonstrate the reliability of a population at consecutive points in time, where a sample at each current point must prove that at least 100% of the devices function until the next point with a probability of at least . To test the reliability of the population, we flexibilise standard lifetime models by allowing the unknown parameter(s) of the corresponding counting process to vary in time. At the same time, we assign a prior distribution that assumes the parameters to be constant within a certain interval. This flexibilisation has several advantages: it can be applied for all parametric lifetimes; its Markov property allows the efficient derivation of the number of defective devices, even for a large number of testing times; and the inference is less certain and hence more realistic and leads to less frequent acceptance of poor quality populations. On the other hand, the inference is stabilised by the informative prior. Based on the flexibilisation of the homogeneous Poisson process (HPP), we derive acceptance sampling plans to test the future reliability of a population. Applying the zero failure sampling plans on simulations of Weibull processes shows their good frequentist properties and their robustness. In the case of utility meters subject to German regulations (Mess- und Eichverordnung (MessEV). 2014: 2010–2073.), application of the derived sequential sampling plans when the conditions of these plans are met can lead to an extension of the verification validity period. These sampling plans protect the consumer better than those from an HPP and are still cost efficient.
Kostenfreier Zugang
Open Access Hybrid
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Bernburg, H., Elster, C., & Klauenberg, K. (2024). Flexible Bayesian reliability demonstration testing. Applied stochastic models in Business and Industry, 40(4), 996–1011.
