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This paper describes Bayesian methods for life test planning with Type II censored data from a Weibull distribution, when the Weibull shape parameter is given. We use conjugate prior distributions and criteria based on estimating a quantile of interest of the lifetime distribution. One criterion is based on a precision factor for a credibility interval for(More)
Most companies maintain warranty databases for purposes of financial reporting and warranty expense forecasting. In some cases, there are attempts to extract engineering information (e.g., on the reliability of components) from such databases. Another important application is to use warranty data to detect potentially serious field reliability problems as(More)
Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved.(More)
This paper evaluates exact coverage probabilities of approximate prediction intervals for the number of failures that will be observed in a future inspection of a sample of units, based only on the results of the rst in-service inspection of the sample. The failure-time of such units is modeled with a Weibull distribution having a given shape parameter(More)
This paper compares diierent procedures to compute conndence intervals for parameters and quantiles of the Weibull, lognormal, and similar log-location-scale distributions from Type I censored data that typically arise from life test experiments. The procedures can be classiied into three groups. The rst group contains procedures based on the commonly-used(More)
Engineers in the manufacturing industries have used accelerated test (AT) experiments for many decades. The purpose of AT experiments is to acquire reliability information quickly. Test units of a material, component, subsystem or entire systems are subjected to higher-than-usual levels of one or more accelerating variables such as temperature or stress.(More)
Modern technological developments such as smart chips, sensors and wireless networks , have changed many data collection processes. For example, there are more and more products being produced with automatic data-collecting devices that track how and under which environments the products are being used. While there is a tremendous amount of dynamic data(More)