Lloyd-Lipow Least Squares Example

From ReliaWiki
Revision as of 17:41, 12 June 2014 by Chris Kahn (talk | contribs)
Jump to navigation Jump to search
RGA Examples Banner.png


New format available! This reference is now available in a new format that offers faster page load, improved display for calculations and images and more targeted search.

As of January 2024, this Reliawiki page will not continue to be updated. Please update all links and bookmarks to the latest references at RGA examples and RGA reference examples.




This example appears in the Reliability Growth and Repairable System Analysis Reference.


After a 20-stage reliability development test program, 20 groups of success/failure data were obtained and are given in the table below. Do the following:

  1. Fit the Lloyd-Lipow model to the data using least squares.
  2. Plot the reliabilities predicted by the Lloyd-Lipow model along with the observed reliabilities, and compare the results.
The Test Results and Reliabilities of Each Stage Calculated from Raw Data and the Predicted Reliability
Test Stage Number([math]\displaystyle{ k\,\! }[/math]) Number of Tests in Stage([math]\displaystyle{ n_k\,\! }[/math]) Number of Successful Tests([math]\displaystyle{ S_k\,\! }[/math]) Raw Data Reliability Lloyd-Lipow Reliability
1 9 6 0.667 0.7002
2 9 5 0.556 0.7369
3 8 7 0.875 0.7552
4 10 6 0.600 0.7662
5 9 7 0.778 0.7736
6 10 8 0.800 0.7788
7 10 7 0.700 0.7827
8 10 6 0.600 0.7858
9 11 7 0.636 0.7882
10 11 9 0.818 0.7902
11 9 9 1.000 0.7919
12 12 10 0.833 0.7933
13 12 9 0.750 0.7945
14 11 8 0.727 0.7956
15 10 7 0.700 0.7965
16 10 8 0.800 0.7973
17 11 10 0.909 0.7980
18 10 9 0.900 0.7987
19 9 8 0.889 0.7992
20 8 7 0.875 0.7998

Solution

  1. The least squares estimates are:
    [math]\displaystyle{ \begin{align} \underset{k=1}{\overset{N}{\mathop \sum }}\,\frac{1}{k}= & \underset{k=1}{\overset{20}{\mathop \sum }}\,\frac{1}{k}=3.5977 \\ \underset{k=1}{\overset{N}{\mathop \sum }}\,\frac{1}{{{k}^{2}}}= & \underset{k=1}{\overset{20}{\mathop \sum }}\,\frac{1}{{{k}^{2}}}=1.5962 \\ \underset{k=1}{\overset{N}{\mathop \sum }}\,\frac{{{S}_{k}}}{{{n}_{k}}}= & \underset{k=1}{\overset{20}{\mathop \sum }}\,\frac{{{S}_{k}}}{{{n}_{k}}}=15.4131 \end{align}\,\! }[/math]
    and:
    [math]\displaystyle{ \underset{k=1}{\overset{N}{\mathop \sum }}\,\frac{{{S}_{k}}}{k\cdot {{n}_{k}}}=\underset{k=1}{\overset{20}{\mathop \sum }}\,\frac{{{S}_{k}}}{k\cdot {{n}_{k}}}=2.5632\,\! }[/math]
    Using these estimates to obtain [math]\displaystyle{ \hat{R_{\infty}}\,\! }[/math] and [math]\displaystyle{ \hat{\alpha}\,\! }[/math]yields:
    [math]\displaystyle{ \begin{align} \text{ }{{\hat{R}}_{\infty }}= &\frac{\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{{{k}^{2}}}\underset{k=1}{\overset{N}{\mathop{\sum }}}\,{{R}_{k}}-\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{k}\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{{{R}_{k}}}{k}}{N\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{{{k}^{2}}}-{{\left( \underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{k} \right)}^{2}}} \\ = & \frac{(1.5962)(15.413)-(3.5977)(2.5637)}{(20)(1.5962)-{{(3.5977)}^{2}}} \\ = & 0.8104 \end{align}\,\! }[/math]
    and:
    [math]\displaystyle{ \begin{align} \hat{\alpha }= &\frac{\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{k}\underset{k=1}{\overset{N}{\mathop{\sum }}}\,{{R}_{k}}-N\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{{{R}_{k}}}{k}}{N\underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{{{k}^{2}}}-{{\left( \underset{k=1}{\overset{N}{\mathop{\sum }}}\,\tfrac{1}{k} \right)}^{2}}}\\ = & \frac{(3.5977)(15.413)-(20)(2.5637)}{(20)(1.5962)-{{(3.5977)}^{2}}} \\ = & 0.2207 \end{align}\,\! }[/math]
    Therefore, the Lloyd-Lipow reliability growth model is as follows, where [math]\displaystyle{ k\,\! }[/math] is the test stage.
    [math]\displaystyle{ {{R}_{k}}=0.8104-\frac{0.2201}{k}\,\! }[/math]
  2. The reliabilities from the raw data and the reliabilities predicted from the Lloyd-Lipow reliability growth model are given in the last two columns of the table. The figure below shows the plot. Based on the given data, the model cannot do much more than to basically fit a line through the middle of the points.
    Rga6.1.png