Since new product designs have little field data available a correlation between field and accelerated test life cannot be made. However, a step partially accelerated life test approach where samples are tested under normal conditions for a time and then run to failure on an accelerated test can be used to estimate the statistical model parameters. This thesis developed the maximum likelihood parameter estimates for a step partially accelerated life test based on a Weibull distribution model for a hypothetical automotive component. Using a Monte Carlo approach with type-II censoring, the effect of sample size and length of sampling period used on the variability of the estimated parameters was examined. A smaller sampling period and small sizes lead to significant variability, which decreased as the sampling period and sample size increased. Use of a partitioned sample did not lead to an improvement in the variability of the estimates. / Department of Mathematical Sciences
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:handle/187873 |
| Date | January 2004 |
| Creators | Brutchen, George W. |
| Contributors | Ali, Mir Masoom |
| Source Sets | Ball State University |
| Detected Language | English |
| Format | ix, 57 leaves : ill. ; 28 cm. |
| Source | Virtual Press |
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