Expected Failure Time Plot: Difference between revisions
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When a reliability life test is planned it is useful to visualize the expected outcome of the experiment. The Expected Failure Time Plot (introduced by ReliaSoft in Weibull++ 8)provides such a visual. | When a reliability life test is planned it is useful to visualize the expected outcome of the experiment. The Expected Failure Time Plot (introduced by ReliaSoft in Weibull++ 8)provides such a visual. | ||
[[Image:EFTP1.png|border|center|700px|Expected Failure Time Plot with a sample size of 5, an assumed Weibull distribution with b=2 and h-1,500 hrs and at a 90% confidence.]]<br> | |||
== Background & Calculations == | == Background & Calculations == | ||
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Using the cumulative binomial, for a defined sample size, one can compute a rank (Median Rank if at 50% probability) for each ordered failure. As an example and for a sample size of 6 the 5%, 50% and 95% ranks would be as follows: | Using the cumulative binomial, for a defined sample size, one can compute a rank (Median Rank if at 50% probability) for each ordered failure. As an example and for a sample size of 6 the 5%, 50% and 95% ranks would be as follows: | ||
<br> | <br> | ||
{| border="1" cellspacing="1" cellpadding="1" width="400" align="center" | {| border="1" cellspacing="1" cellpadding="1" width="400" align="center" | ||
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| valign="middle" align="center" | 60.70% | | valign="middle" align="center" | 60.70% | ||
| valign="middle" align="center" | | | valign="middle" align="center" | | ||
89.09% | 89.09% | ||
| valign="middle" align="center" | | | valign="middle" align="center" | | ||
99.15% | 99.15% | ||
|} | |} | ||
<br> | <br> | ||
Furthermore, consider that for the units to be tested the underlying reliability model assumption is given by a Weibull distribution with <span class="texhtml">β = 2</span>, and <span class="texhtml">η = 100</span> hr. Then the median time to failure of the first unit on test can be determined by solving the Weibull reliability equation for t, at each probability, | Furthermore, consider that for the units to be tested the underlying reliability model assumption is given by a Weibull distribution with <span class="texhtml">β = 2</span>, and <span class="texhtml">η = 100</span> hr. Then the median time to failure of the first unit on test can be determined by solving the Weibull reliability equation for t, at each probability, | ||
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then for 0.85%, | then for 0.85%, | ||
<br>1-0.0085=e^{\big({t \over 100}\big)^2} | <br>1-0.0085=e^{\big({t \over 100}\big)^2} | ||
<br> | |||
and so forths as shown in the table below: | and so forths as shown in the table below: | ||
<br> | <br> | ||
{| border="1" cellspacing="1" cellpadding="1" width="400" align="center" | {| border="1" cellspacing="1" cellpadding="1" width="400" align="center" | ||
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| valign="middle" align="center" | 6 | | valign="middle" align="center" | 6 | ||
| valign="middle" align="center" | | | valign="middle" align="center" | | ||
96.64 | 96.64 | ||
| valign="middle" align="center" | 148.84 | | valign="middle" align="center" | 148.84 | ||
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|} | |} | ||
<br><br> | <br><br> | ||
<br> | <br> | ||
<br> | <br> | ||
<br> | <br> | ||
<br><br> | <br><br> | ||
<a _fcknotitle="true" href="Category:Weibull++">Weibull++</a> <a _fcknotitle="true" href="Category:Test_Design">Test_Design</a> <a _fcknotitle="true" href="Category:Special_Tools">Special_Tools</a> | <a _fcknotitle="true" href="Category:Weibull++">Weibull++</a> <a _fcknotitle="true" href="Category:Test_Design">Test_Design</a> <a _fcknotitle="true" href="Category:Special_Tools">Special_Tools</a> |
Revision as of 11:20, 10 March 2011
Expected Failure Time Plot
When a reliability life test is planned it is useful to visualize the expected outcome of the experiment. The Expected Failure Time Plot (introduced by ReliaSoft in Weibull++ 8)provides such a visual.
Background & Calculations
Using the cumulative binomial, for a defined sample size, one can compute a rank (Median Rank if at 50% probability) for each ordered failure. As an example and for a sample size of 6 the 5%, 50% and 95% ranks would be as follows:
Order Number | 5% | 50% | 95% |
---|---|---|---|
1 | 0.85% | 10.91% | 39.30% |
2 | 6.29% | 26.45% | 58.18% |
3 | 15.32% | 42.14% | 72.87% |
4 | 27.13% | 57.86% | 84.68% |
5 | 41.82% | 73.55% | 93.71% |
6 | 60.70% |
89.09% |
99.15% |
Furthermore, consider that for the units to be tested the underlying reliability model assumption is given by a Weibull distribution with β = 2, and η = 100 hr. Then the median time to failure of the first unit on test can be determined by solving the Weibull reliability equation for t, at each probability,
or
R(t)=e^{\big({t \over \eta}\big)^\beta}
then for 0.85%,
1-0.0085=e^{\big({t \over 100}\big)^2}
and so forths as shown in the table below:
Order Number | Lowest Expected Time-to-failure (hr) | Median Expected Time-to-failure (hr) | Highest Expected Time-to-failure (hr) |
---|---|---|---|
1 | 9.25 | 33.99 | 70.66 |
2 | 25.48 | 55.42 | 93.37 |
3 | 40.77 | 73.97 | 114.21 |
4 | 56.26 | 92.96 | 136.98 |
5 | 73.60 | 115.33 | 166.34 |
6 |
96.64 |
148.84 | 218.32 |
<a _fcknotitle="true" href="Category:Weibull++">Weibull++</a> <a _fcknotitle="true" href="Category:Test_Design">Test_Design</a> <a _fcknotitle="true" href="Category:Special_Tools">Special_Tools</a>