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Quality and Reliability Methods - SAS

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Chapter 14 Lifetime Distribution II 239<br />

Introduction to Accelerated Test Models<br />

Introduction to Accelerated Test Models<br />

The Fit Life by X platform provides the tools needed for accelerated life-testing analysis. Accelerated tests are<br />

routinely used in industry to provide failure-time information about products or components in a relatively<br />

short-time frame. Common accelerating factors include temperature, voltage, pressure, <strong>and</strong> usage rate.<br />

Results are extrapolated to obtain time-to-failure estimates at lower, normal operating levels of the<br />

accelerating factors. These results are used to assess reliability, detect <strong>and</strong> correct failure modes, compare<br />

manufacturers, <strong>and</strong> certify components.<br />

The Fit Life by X platform includes many commonly used transformations to model physical <strong>and</strong> chemical<br />

relationships between the event <strong>and</strong> the factor of interest. Examples include transformation using Arrhenius<br />

relationship time-acceleration factors <strong>and</strong> Voltage-acceleration mechanisms. Linear, Log, Logit,<br />

Reciprocal, Square Root, Box-Cox, Location, Location <strong>and</strong> Scale, <strong>and</strong> Custom acceleration models are<br />

also included in this platform.<br />

You can use the DOE > Accelerated Life Test Design platform to design accelerated life test experiments.<br />

Meeker <strong>and</strong> Escobar (1998, p. 495) offer a strategy for analyzing accelerated lifetime data:<br />

1. Examine the data graphically. One useful way to visualize the data is by examining a scatterplot of the<br />

time-to-failure variable versus the accelerating factor.<br />

2. Fit distributions individually to the data at different levels of the accelerating factor. Repeat for different<br />

assumed distributions.<br />

3. Fit an overall model with a plausible relationship between the time-to-failure variable <strong>and</strong> the<br />

accelerating factor.<br />

4. Compare the model in Step 3 with the individual analyses in Step 2, assessing the lack of fit for the<br />

overall model.<br />

5. Perform residual <strong>and</strong> various diagnostic analyses to verify model assumptions.<br />

6. Assess the plausibility of the data to make inferences.<br />

Launching the Fit Life by X Platform Window<br />

This example uses Devalt.jmp, from Meeker <strong>and</strong> Escobar (1998), <strong>and</strong> can be found in the <strong>Reliability</strong> folder<br />

of the sample data. It contains time-to-failure data for a device at accelerated operating temperatures. No<br />

time-to-failure observation is recorded for the normal operating temperature of 10 degrees Celsius; all other<br />

observations are shown as time-to-failure or censored values at accelerated temperature levels of 40, 60, <strong>and</strong><br />

80 degrees Celsius.<br />

1. Open the Devalt.jmp sample data table.<br />

2. Select Analyze > <strong>Reliability</strong> <strong>and</strong> Survival > Fit Life by X.<br />

3. Select Hours as Y, Time to Event.<br />

4. Select Temp as X.<br />

5. Select Censor as Censor.

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