Non-Parametric RDA Transmission Example

Non-Parametric Recurrent Event Data Analysis Transmission Example

The following table shows the repairs on a sample of 14 cars with manual transmission in a preproduction road test [31]. Here + denotes the censoring ages (how long a car has been observed). $$\begin{matrix} Car ID & Mileage \\ \text{1} & \text{27099+} \\ \text{2} & \text{21999+} \\ \text{3} & \text{11891, 27583+} \\ \text{4} & \text{19966+} \\ \text{5} & \text{26146+} \\ \text{6} & \text{3648, 13957, 23193+} \\ \text{7} & \text{19823+} \\ \text{8} & \text{2890, 22707+} \\ \text{9} & \text{2714, 19275+} \\ \text{10} & \text{19803+} \\ \text{11} & \text{19630+} \\ \text{12} & \text{22056+} \\ \text{13} & \text{22940+} \\ \text{14} & \text{3240, 7690, 18965+} \\ \end{matrix}$$ The car manufacturer seeks to estimate the mean cumulative number of repairs per car by 24,000 test miles (equivalently 5.5 x 24,000 = 132,000 customer miles) and to observe whether the population repair rate increases or decreases as a population ages.

Solution

The data is entered into a non-parametric RDA folio in Weibull++ as follows.



The results are as follows,



The results indicate that after 13,957 miles of testing, the estimated mean cumulative number of repairs per car is 0.5. Therefore, by 24,000 test miles, the estimated mean cumulative number of repairs per car is 0.5.

The MCF plot is shown next.



A smooth curve through the MCF plot has a derivative that decreases as the population ages. That is, the repair rate decreases as each population ages. This is typical of products with manufacturing defects.