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Consideration of Uncertainty in Reliability Demonstration Test Planning
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Statistical power analyses are used in the design of experiments to determine the required number of specimens, and thus the expenditure, of a test. Commonly, when analyzing and planning life tests of technical products, only the confidence level is taken into account for assessing uncertainty. However, due to the sampling error, the confidence interval estimation varies from test to test; therefore, the number of specimens needed to yield a successful reliability demonstration cannot be derived by this. In this paper, a procedure is presented that facilitates the integration of statistical power analysis into reliability demonstration test planning. The Probability of Test Success is introduced as a metric in order to place the statistical power in the context of life test planning of technical products. It contains the information concerning the probability that a life test is capable of demonstrating a required lifetime, reliability, and confidence. In turn, it enables the assessment and comparison of various life test types, such as success run, non-censored, and censored life tests. The main results are four calculation methods for the Probability of Test Success for various test scenarios: a general method which is capable of dealing with all possible scenarios, a calculation method mimicking the actual test procedure, and two analytic approaches for failure-free and failure-based tests which make use of the central limit theorem and asymptotic properties of several statistics, and therefore simplify the effort involved in planning life tests. The calculation methods are compared and their respective advantages and disadvantages worked out; furthermore, the scenarios in which each method is to be preferred are illustrated. The applicability of the developed procedure for planning reliability demonstration tests using the Probability of Test Success is additionally illustrated by a case study.
In this paper, we conduct Monte Carlo simulation studies in order to assess the coverage probability of bias-corrected confidence bounds for the Weibull distribution. It is shown that coverage probabilities of confidence bounds are highly dependent on the chosen bias-correction method. Based on simulation results, recommendations for choosing the best performing combinations of bias-corrections and confidence bounds are given.
R-OPTIMA -Optimal Planning of Reliability Tests
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Dazer, M., Herzig, T., Grundler, A., Bertsche, B.
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International Conference Integrity-Reliability-Failure, Funchal/Portugal 14-18 June 2020.
Editors J.F. Silva Gomes and S.A. Meguid, Publ.
INEGI/FEUP (2020), pp.695-702, ISBN: 978-989-20-8315-3
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Herzig, T., Dazer, M., Grundler, A., Bertsche, B.
(2020) Integrating Accelerated Life Tests into
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International Conference Integrity-Reliability-Failure, Funchal/Portugal 14-18 June 2020.
Editors J.F. Silva Gomes and S.A. Meguid, Publ.
INEGI/FEUP (2020), pp.665-672, ISBN: 978-989-20-8315-3
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