Enter your data for Accelerated Life Testing

Stat > Reliability/Survival > Accelerated Life Testing

Enter your data

Select the option that best describes your data.

Responses are uncens/right censored data

If you have exact failure times, right-censored observations (you know only the time after which the failure occurred), or both, complete the following steps. For more information on censored data, go to Data censoring.

  1. In Variables/Start variables, enter the column that contains the failure times or censoring times. You can enter up to 10 columns (for 10 different samples).
  2. If you have frequency data for each variable, in Freq. columns (optional), enter a column that indicates the number of units for each failure time or censoring time.
  3. In Accelerating var, enter the column that contains the values of the predictor variable that was used to accelerate the failures.
  4. From Relationship, select an option to indicate whether to transform the accelerating variable. If you can assume a linear relationship, select Linear and no transformation is performed. To transform the accelerating variable, select Arrhenius, Inverse temp, or Ln (Power).
    Note

    For information on relationship models and transformations of the accelerating variable, go to Choose the appropriate model for accelerated life testing.

In this worksheet, the Time column contains the failure times. The Censor column contains the censoring indicators: an F designates an actual failure time; a C designates a unit that was removed from the test, and thus censored. The Temp column contains the values for the accelerating variable, which that was used to accelerate the failures.
C1 C2 C3
Time Censor Temperature
53 F 125
60 F 125
53 F 125
99 C 125
51 F 150
40 F 150

Responses are uncens/arbitrarily censored data

If your data include left-censored observations (you know only the time before which the failure occurred), interval-censored observations (you know only the times between which the failure occurred), or a varied censoring scheme that includes exact failure times, right censoring, left censoring, and interval censoring, complete the following steps. For more information on censored data, go to Data censoring.

  1. In Variables/Start variables, enter the column that contains the start times. You can enter up to 10 columns (for 10 different samples). The start times in the column depend on how the data are censored.
    Observation Value in Start column
    Exact failure time Failure time
    Right censored Time after which the failure occurred
    Left censored * (missing value symbol)
    Interval censored Time at start of interval during which the failure occurred
  2. In End variables, enter the column that contains the end times. You can enter up to 10 columns (for 10 different samples). The end times in the column depend on how the data are censored.
    Observation Value in End column
    Exact failure time Failure time
    Right censored * (missing value symbol)
    Left censored Time before which the failure occurred
    Interval censored Time at end of interval during which the failure occurred
  3. If you have frequency data for each variable, in Freq. columns (optional), enter a column that indicates the number of units for each failure time or censoring time.
  4. In Accelerating var, enter the column that contains the values of the predictor variable that was used to accelerate the failures.
  5. From Relationship, select an option to indicate whether to transform the accelerating variable. If you can assume a linear relationship, select Linear and no transformation is performed. To transform the accelerating variable, select Arrhenius, Inverse temp, or Ln (Power).
    Note

    For information on relationship models and transformations of the accelerating variable, go to Choose the appropriate model for accelerated life testing.

In this worksheet, the Start column contains the start times and the End column contains the end times. The Temp column contains the values for the accelerating variable, which that was used to accelerate the failures. The Frequency column (optional) indicates the number of units that are included in each interval. For example, at a temperature of 125°, 20 units are left censored at 10,000 hours. 2 units are exact failures at 30000 hours. 26 units are interval censored between 30,000 and 40,000 hours. 190 units are right censored at 50,000 hours.

C1 C2 C3 C5
Start End Frequency Temp
* 10000 20 125
10000 20000 10 125
20000 30000 10 125
30000 30000 2 125
30000 40000 26 125
40000 50000 42 125
50000 * 190 125
* 10000 22 150
10000 20000 14 150
20000 30000 15 150
30000 40000 33 150
40000 50000 55 150
50000 * 161 150

Second Variable

Select if you want to include a second accelerating variable in the model. Then enter additional information about the variable.
  • Accelerating: If the second variable is an accelerating variable, with extreme levels that are designed to accelerate the lifetime of each unit, select this option, then enter the column of predictor levels used for the test.
    Relationship
    Select a relationship to indicate whether to transform the accelerating variable. If you can assume a linear relationship, select Linear and no transformation is performed. To transform the accelerating variable, select Arrhenius, Inverse temp, or Ln (Power).
    Note

    For information on relationship models and transformations of the accelerating variable, go to Choose the appropriate model for accelerated life testing.

  • Factor: If the second variable is a factor with categorical levels, such as conditions or grouping information, select this option. Then enter the column that contains the factor levels.
Include interaction term between variables
Select to include an interaction term between the accelerating variable and the second variable. An interaction occurs when the failure time of a unit at one level of the accelerating variable depends on the level of the second variable.

Assumed distribution

Select a distribution to model your data. Base your decision on process knowledge or use probability plots to evaluate the model fit. For more information, go to Distribution fit for reliability analysis.

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