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Accelerated Life TestingStat > Reliability/Survival > Accelerated Life Testing
Use accelerated life testing to investigate the relationship between failure time and one or two predictors. The first predictor is an accelerating variable. The second predictor can be either a second accelerating variable or a factor.
The most common application of accelerated life testing is for studies in which you impose a series of variable levels far exceeding normal field conditions to accelerate the failure process. The variable is thus called the accelerating variable. Accelerated tests are performed to save time and money, since under normal field conditions, it can take a very long time for a unit to fail. Accelerated life testing requires knowledge of the relationship between the accelerating variable(s) and failure times.
Here are the steps:
1 Impose levels of the accelerating variable(s) on the units.
2 Record failure (or censoring) times.
3 Run the Accelerated Life Testing analysis, asking Minitab to extrapolate to the design value, or common field condition. This way, you can find out how the units behave under normal field conditions.
The simplest output includes a regression table, relation plot, and probability plot for each level of the accelerating variable(s) based on the fitted model. The relation plot displays the relationship between the accelerating variable(s) and failure time by plotting percentiles for each level of the accelerating variable(s). By default, lines are drawn at the 10th, 50th, and 90th percentiles. The 50th percentile is a good estimate for the time a part will last when exposed to various levels of the accelerating variable(s). The probability plot is created for each level of the accelerating variable(s) based on the fitted model (line) and based on the nonparametric model (points).
Responses are uncens/right censored data: Choose if your data is uncensored or right censored.
Responses are uncens/arbitrarily censored data: Choose if your data is uncensored or arbitrarily censored.
Variables/Start variables: Enter up to 10 columns (10 different samples) containing the start times.
End variables: If you have uncensored or arbitrarily censored data, enter up to 10 columns (10 different samples) of end times.
Freq. columns (optional): Enter a column containing frequency data for each variable.
Accelerating var: Enter the column containing the predictor values.
Relationship: Choose a linear (no transformation), Arrhenius, inverse temperature, or ln (power) transformation for the accelerating variable. By default, Minitab assumes the relationship is linear.
Second Variable:
Accelerating: Enter the column containing the predictor values for the second accelerating variable.
Relationship: Choose a linear (no transformation), Arrhenius, inverse temperature, or ln (power) transformation for the accelerating variable. By default, Minitab assumes the relationship is linear.
Factor: Enter the column containing the factor levels.
Include interaction term between variables: Check to include an interaction term between the accelerating variable and the second variable.
Assumed distribution: Choose one of eight common lifetime distributions: Weibull (default), smallest extreme value, exponential, normal, lognormal, logistic, and loglogistic.