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ALTA Life Stress Data Folio

ReliaSoft Plot Utilities

ALTA Plots

To create a plot in an ALTA life-stress data folio, choose Life-Stress Data > Analysis > Plot or click the icon on the Main page of the control panel.

Tip: You can add additional plot sheets to the folio by choosing Life-Stress Data > Folio Sheets > Insert Additional Plot. The additional sheets can function as overlay plots to display results from multiple data sheets in the current folio on a single plot.

The following is a description of the different types of plots that can be created in an ALTA life-stress data folio. (For general information on working with plots, see ReliaSoft Plot Utilities.)

Note that the use stress level for plots is by default the level you entered on the Main page of the control panel. However, you may adjust the level for plots by clicking Set Use Stress directly underneath the Analysis Summary area of the Plot page. This will not change the use stress level specified on the Main page.

Note: Unlike the probability plots for other distributions, the y-axis in an exponential probability plot always indicates the reliability instead of the unreliability. This tradition arose from the time when probability plotting was performed "by hand." The exponential reliability model starts with R = 1 at T = 0 (or gamma). Thus, if the unreliability were plotted, the axis would start at Q = 1 - R = 0, which is not possible, given that the y-axis scale is logarithmic.

If a model that uses the Weibull distribution is selected, an eta line will be displayed as well. The eta line estimates the time by which 63.2% of units in the population are expected to fail. The plot may also include other life lines that show the relationship between stress level and the time by which other specified percentages of a population are expected to fail (see Specifying Life Lines.) The life-stress relationship is linearized whenever possible.

For example, the next figures show the results of an Arrhenius-Weibull model and an Arrhenius-lognormal model using the same data set. As you can see, the plot shows that the lognormal distribution presents the better fit to this particular data set.

 

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