Abstract
| Language | English |
|---|---|
| Pages | 365-383 |
| Number of pages | 18 |
| Journal | International Journal of Reliability, Qualily and Safety Engineering |
| Volume | 12 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2005 |
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Keywords
- reliability engineering
- management thory
- reliability growth models
- statistics
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Optimal discrete stopping times for reliability growth tests. / Quigley, J.L.
In: International Journal of Reliability, Qualily and Safety Engineering, Vol. 12, No. 5, 2005, p. 365-383.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimal discrete stopping times for reliability growth tests
AU - Quigley, J.L.
N1 - Electronic version of an article published as Int. J. Rel. Qual. Saf. Eng., 12, 365, (2005) © Copyright World Scientific Publishing Company.
PY - 2005
Y1 - 2005
N2 - Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided.
AB - Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided.
KW - reliability engineering
KW - management thory
KW - reliability growth models
KW - statistics
UR - http://www.worldscientific.com/worldscinet/ijrqse
U2 - 10.1142/S0218539305001896
DO - 10.1142/S0218539305001896
M3 - Article
VL - 12
SP - 365
EP - 383
JO - International Journal of Reliability, Qualily and Safety Engineering
T2 - International Journal of Reliability, Qualily and Safety Engineering
JF - International Journal of Reliability, Qualily and Safety Engineering
SN - 0218-5393
IS - 5
ER -