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 &amp; Calculations  ==
== Background &amp; 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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&lt;a _fcknotitle="true" href="Category:Weibull++"&gt;Weibull++&lt;/a&gt; &lt;a _fcknotitle="true" href="Category:Test_Design"&gt;Test_Design&lt;/a&gt; &lt;a _fcknotitle="true" href="Category:Special_Tools"&gt;Special_Tools&lt;/a&gt;
&lt;a _fcknotitle="true" href="Category:Weibull++"&gt;Weibull++&lt;/a&gt; &lt;a _fcknotitle="true" href="Category:Test_Design"&gt;Test_Design&lt;/a&gt; &lt;a _fcknotitle="true" href="Category:Special_Tools"&gt;Special_Tools&lt;/a&gt;

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:


Table 1: 5%, 50% and 95% Ranks for a sample size of 6. 
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:


Table 2: Times corresponding to the 5%, 50% and 95% Ranks for a sample size of 6. and assuming Weibull distribution with β = 2, and η = 100 hr.
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>