Difference between revisions of "Template:Example: Recurrent Events Data Parameteric Air-Condition Example"

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[[Recurrent Event Data Analysis Parametric Air-Condition Example]]
 
 
'''Recurrent Event Data Analysis Parametric Air-Condition Example'''  
 
'''Recurrent Event Data Analysis Parametric Air-Condition Example'''  
  

Revision as of 17:33, 23 March 2012

Recurrent Event Data Analysis Parametric Air-Condition Example

The following table gives the failure times of the air-condition unit of an aircraft. The observation ended by the time the last failure occurred. [3]

[math]\begin{matrix} \text{50} & \text{329} & \text{811} & \text{991} & \text{1489} \\ \text{94} & \text{332} & \text{899} & \text{1013} & \text{1512} \\ \text{196} & \text{347} & \text{945} & \text{1152} & \text{1525} \\ \text{268} & \text{544} & \text{950} & \text{1362} & \text{1539} \\ \text{290} & \text{732} & \text{955} & \text{1459} & {} \\ \end{matrix}[/math]
1. Estimate the GRP model parameters using the Type I virtual age option.
2. Plot the failure number and instantaneous failure intensity vs. time with 90% two-sided confidence bounds.
3. Plot the conditional reliability vs. time with 90% two-sided confidence bounds. The mission start time is 40 and mission time is varying.
4. Using the QCP, calculate the expected failure number and expected instantaneous failure intensity by time 1800.


Solution

Enter the data into a parametric RDA folio in Weibull++. Choose 3 under Parameters and Type I under Settings. Keep the default simulation settings.

1. The estimated parameters are [math]\hat{\beta }=1.1976,[/math] [math]\hat{\lambda }=4.94E-03,[/math] [math]\hat{q}=0.1344[/math] .
2. The failure number and instantaneous failure intensity are given in the following plots.
Parametric RDA N(T) plot.png
Parametric RDA Lambda(T) plot.png
3. The conditional reliability is plotted below.
Parametric RDA Cond R(T) plot.png
4. Using QCP, the failure number and instantaneous failure intensity are:
QCP N(T).png
QCP Lambda(T).png