Weibull analysis is a tool that is used for characterizing the behavior of a data set.  The value of characterizing a data set is being able to make predictions of failure rate for the same or a similar population. Input to the Weibull analysis is time to fail for all failed units and running time for operating units. The assumptions for the data set are critical to ensuring the right information is included in the analysis.  This may include root cause analysis of the failures so they can be characterized as wear-out or use stress related. It would be beneficial to separate these two data sets because their characterization and behavior will be quite different Weibull factors. [Read more…]

If you had to section the history of reliability into only two periods I believe the dividing point would be around 1950.  The first section, stone age to 1950, the “Robust” era; 1950 to today, the “Margin” era.   In the robust, era reliability concerns were addressed by creating a design that was orders of magnitude stronger than required, “over-engineered”.  This protected against the variability of fabrication, use, and environment.  If it was made sloppily with low tolerances, it still worked;  Used for an unintended purpose, still worked; Left out in the snow, dropped in the river, run over by a horse, buried in the sand; still worked. [Read more…]