Test-Find-Test Data Example: Difference between revisions
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Do the following: | Do the following: | ||
#Determine the projected MTBF and failure intensity. | |||
#Determine the growth potential MTBF and failure intensity. | |||
#Determine the demonstrated MTBF and failure intensity. | |||
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'''Solution''' | '''Solution''' | ||
<ol> | |||
<li>The maximum likelihood estimates of <math>{{\beta }_{BD}}\,\!</math> and <math>{{\lambda }_{BD}}\,\!</math> are determined to be: | |||
:<math>\begin{align} | :<math>\begin{align} | ||
{{{\hat{\beta }}}_{BD}} = & \frac{M}{\underset{i=1}{\overset{M}{\mathop{\sum }}}\,\ln (\tfrac{T}{{{X}_{i}}})} \\ | {{{\hat{\beta }}}_{BD}} = & \frac{M}{\underset{i=1}{\overset{M}{\mathop{\sum }}}\,\ln (\tfrac{T}{{{X}_{i}}})} \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The unbiased estimate of <math>\beta \,\!</math> is: | The unbiased estimate of <math>\beta \,\!</math> is: | ||
:<math>\begin{align} | :<math>\begin{align} | ||
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= & 0.7472 | = & 0.7472 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
Based on the test data, <math>\overline{d}=\tfrac{1}{M}\underset{i=1}{\overset{M}{\mathop{\sum }}}\,{{d}_{i}}= 0.72125\,\!</math>. Therefore, <math>B(T)=\overline{d}\tfrac{M{{\overline{\beta }}_{BD}}}{T}=0.0215\,\!</math>. The projected failure intensity due to incorporating the 16 corrective actions is: | Based on the test data, <math>\overline{d}=\tfrac{1}{M}\underset{i=1}{\overset{M}{\mathop{\sum }}}\,{{d}_{i}}= 0.72125\,\!</math>. Therefore, <math>B(T)=\overline{d}\tfrac{M{{\overline{\beta }}_{BD}}}{T}=0.0215\,\!</math>. The projected failure intensity due to incorporating the 16 corrective actions is: | ||
:<math>\begin{align} | :<math>\begin{align} | ||
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= & 0.0661 | = & 0.0661 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The projected MTBF is: | The projected MTBF is: | ||
:<math>M\widehat{T}B{{F}_{P}}={{[r(T)]}^{-1}}=15.127\,\!</math> | :<math>M\widehat{T}B{{F}_{P}}={{[r(T)]}^{-1}}=15.127\,\!</math> | ||
</li> | |||
<li>To estimate the maximum reliability that can be attained with this management strategy, use the following calculations. | |||
:<math>\begin{align} | :<math>\begin{align} | ||
{{N}_{A}}/T=0.0250 | {{N}_{A}}/T=0.0250 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\frac{1}{T}\underset{i=1}{\overset{16}{\mathop \sum }}\,(1-{{d}_{i}}){{N}_{i}}=0.0196\,\!</math> | :<math>\frac{1}{T}\underset{i=1}{\overset{16}{\mathop \sum }}\,(1-{{d}_{i}}){{N}_{i}}=0.0196\,\!</math> | ||
The growth potential failure intensity is estimated by: | The growth potential failure intensity is estimated by: | ||
:<math>\begin{align} | :<math>\begin{align} | ||
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= & 0.0446 | = & 0.0446 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The growth potential MTBF is: | The growth potential MTBF is: | ||
:<math>M\widehat{T}B{{F}_{GP}}={{[{{\widehat{r}}_{GP}}]}^{-1}}=22.4467\,\!</math> | :<math>M\widehat{T}B{{F}_{GP}}={{[{{\widehat{r}}_{GP}}]}^{-1}}=22.4467\,\!</math> | ||
</li> | |||
<li>The demonstrated failure intensity and MTBF are estimated by: | |||
:<math>\begin{align} | :<math>\begin{align} | ||
{{\widehat{\lambda }}_{D}}(T) = & \frac{{{N}_{A}}+{{N}_{BD}}}{T} \\ | {{\widehat{\lambda }}_{D}}(T) = & \frac{{{N}_{A}}+{{N}_{BD}}}{T} \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | :<math>\begin{align} | ||
M\widehat{T}B{{F}_{D}} = & {{[{{\widehat{\lambda }}_{D}}(T)]}^{-1}} \\ | M\widehat{T}B{{F}_{D}} = & {{[{{\widehat{\lambda }}_{D}}(T)]}^{-1}} \\ | ||
= & 9.5238 | = & 9.5238 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The first chart below shows the demonstrated, projected and growth potential MTBF. The second shows the demonstrated, projected and growth potential failure intensity. | The first chart below shows the demonstrated, projected and growth potential MTBF. The second shows the demonstrated, projected and growth potential failure intensity. | ||
[[Image:rga9.3.png|center|400px|Demonstrated, projected and growth potential MTBF.]] | [[Image:rga9.3.png|center|400px|Demonstrated, projected and growth potential MTBF.]] | ||
[[Image:rga9.4.png|center|400px|Demonstrated, projected and growth potential failure intensity.]] | [[Image:rga9.4.png|center|400px|Demonstrated, projected and growth potential failure intensity.]] | ||
</li> | |||
</ol> |
Revision as of 23:17, 22 April 2014
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This example appears in the Reliability Growth and Repairable System Analysis Reference book.
Consider the data in the first table below. A system was tested for [math]\displaystyle{ T=400\,\! }[/math] hours. There were a total of [math]\displaystyle{ N=42\,\! }[/math] failures and all corrective actions will be delayed until after the end of the 400 hour test. Each failure has been designated as either an A failure mode (the cause will not receive a corrective action) or a BD mode (the cause will receive a corrective action). There are [math]\displaystyle{ {{N}_{A}}=10\,\! }[/math] A mode failures and [math]\displaystyle{ {{N}_{BD}}=32\,\! }[/math] BD mode failures. In addition, there are [math]\displaystyle{ M=16\,\! }[/math] distinct BD failure modes, which means 16 distinct corrective actions will be incorporated into the system at the end of test. The total number of failures for the [math]\displaystyle{ {{j}^{th}}\,\! }[/math] observed distinct BD mode is denoted by [math]\displaystyle{ {{N}_{j}}\,\! }[/math], and the total number of BD failures during the test is [math]\displaystyle{ {{N}_{BD}}=\underset{j=1}{\overset{M}{\mathop{\sum }}}\,{{N}_{j}}\,\! }[/math]. These values and effectiveness factors are given in the second table.
Do the following:
- Determine the projected MTBF and failure intensity.
- Determine the growth potential MTBF and failure intensity.
- Determine the demonstrated MTBF and failure intensity.
Test-Find-Test Data | ||||||
[math]\displaystyle{ i\,\! }[/math] | [math]\displaystyle{ {{X}_{i}}\,\! }[/math] | Mode | [math]\displaystyle{ i\,\! }[/math] | [math]\displaystyle{ {{X}_{i}}\,\! }[/math] | Mode | |
---|---|---|---|---|---|---|
1 | 15 | BD1 | 22 | 260.1 | BD1 | |
2 | 25.3 | BD2 | 23 | 263.5 | BD8 | |
3 | 47.5 | BD3 | 24 | 273.1 | A | |
4 | 54 | BD4 | 25 | 274.7 | BD6 | |
5 | 56.4 | BD5 | 26 | 285 | BD13 | |
6 | 63.6 | A | 27 | 304 | BD9 | |
7 | 72.2 | BD5 | 28 | 315.4 | BD4 | |
8 | 99.6 | BD6 | 29 | 317.1 | A | |
9 | 100.3 | BD7 | 30 | 320.6 | A | |
10 | 102.5 | A | 31 | 324.5 | BD12 | |
11 | 112 | BD8 | 32 | 324.9 | BD10 | |
12 | 120.9 | BD2 | 33 | 342 | BD5 | |
13 | 125.5 | BD9 | 34 | 350.2 | BD3 | |
14 | 133.4 | BD10 | 35 | 364.6 | BD10 | |
15 | 164.7 | BD9 | 36 | 364.9 | A | |
16 | 177.4 | BD10 | 37 | 366.3 | BD2 | |
17 | 192.7 | BD11 | 38 | 373 | BD8 | |
18 | 213 | A | 39 | 379.4 | BD14 | |
19 | 244.8 | A | 40 | 389 | BD15 | |
20 | 249 | BD12 | 41 | 394.9 | A | |
21 | 250.8 | A | 42 | 395.2 | BD16 |
Effectiveness Factors for the Unique BD Modes | |||
BD Mode | Number [math]\displaystyle{ {{N}_{j}}\,\! }[/math] | First Occurrence | EF [math]\displaystyle{ {{d}_{i}}\,\! }[/math] |
---|---|---|---|
1 | 2 | 15.0 | .67 |
2 | 3 | 25.3 | .72 |
3 | 2 | 47.5 | .77 |
4 | 2 | 54.0 | .77 |
5 | 3 | 54.0 | .87 |
6 | 2 | 99.6 | .92 |
7 | 1 | 100.3 | .50 |
8 | 3 | 112.0 | .85 |
9 | 3 | 125.5 | .89 |
10 | 4 | 133.4 | .74 |
11 | 1 | 192.7 | .70 |
12 | 2 | 249.0 | .63 |
13 | 1 | 285.0 | .64 |
14 | 1 | 379.4 | .72 |
15 | 1 | 389.0 | .69 |
16 | 1 | 395.2 | .46 |
Solution
- The maximum likelihood estimates of [math]\displaystyle{ {{\beta }_{BD}}\,\! }[/math] and [math]\displaystyle{ {{\lambda }_{BD}}\,\! }[/math] are determined to be:
- [math]\displaystyle{ \begin{align} {{{\hat{\beta }}}_{BD}} = & \frac{M}{\underset{i=1}{\overset{M}{\mathop{\sum }}}\,\ln (\tfrac{T}{{{X}_{i}}})} \\ = & 0.7970 \\ {{{\hat{\lambda }}}_{BD}} = & 0.1350 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} {{\overline{\beta }}_{BD}} = & \frac{M-1}{M}{{{\hat{\beta }}}_{BD}} \\ = & 0.7472 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} r(T) = & \left( \frac{{{N}_{A}}}{T}+\underset{i=1}{\overset{M}{\mathop \sum }}\,(1-{{d}_{i}})\frac{{{N}_{i}}}{T} \right)+\overline{d}\left( \frac{M}{T}{{\overline{\beta }}_{BD}} \right) \\ = & 0.0661 \end{align}\,\! }[/math]
- [math]\displaystyle{ M\widehat{T}B{{F}_{P}}={{[r(T)]}^{-1}}=15.127\,\! }[/math]
- To estimate the maximum reliability that can be attained with this management strategy, use the following calculations.
- [math]\displaystyle{ \begin{align} {{N}_{A}}/T=0.0250 \end{align}\,\! }[/math]
- [math]\displaystyle{ \frac{1}{T}\underset{i=1}{\overset{16}{\mathop \sum }}\,(1-{{d}_{i}}){{N}_{i}}=0.0196\,\! }[/math]
- [math]\displaystyle{ \begin{align} {{\widehat{r}}_{GP}}(T) = & \left( \frac{{{N}_{A}}}{T}+\underset{i=1}{\overset{M}{\mathop \sum }}\,(1-{{d}_{i}})\frac{{{N}_{i}}}{T} \right) \\ = & 0.0250+0.0196 \\ = & 0.0446 \end{align}\,\! }[/math]
- [math]\displaystyle{ M\widehat{T}B{{F}_{GP}}={{[{{\widehat{r}}_{GP}}]}^{-1}}=22.4467\,\! }[/math]
- The demonstrated failure intensity and MTBF are estimated by:
- [math]\displaystyle{ \begin{align} {{\widehat{\lambda }}_{D}}(T) = & \frac{{{N}_{A}}+{{N}_{BD}}}{T} \\ = & \frac{42}{400} \\ = & 0.1050 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} M\widehat{T}B{{F}_{D}} = & {{[{{\widehat{\lambda }}_{D}}(T)]}^{-1}} \\ = & 9.5238 \end{align}\,\! }[/math]