Non-Parametric RDA Transmission Example

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This example appears in the Life Data Analysis Reference book.

Transmission Example

The following table shows the repairs in a pre-production road test on a sample of 14 cars with manual transmissions [31]. Here, the + sign denotes the censoring ages (how long a car has been observed).

[math]\displaystyle{ \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} }[/math]

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 the population ages.

Solution

Enter the data 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.