Crow-AMSAA Confidence Bounds Example: Difference between revisions
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Using the values of <math>\hat{\beta }\,\!</math> and <math>\hat{\lambda }\,\!</math> estimated in the <noinclude>[[Crow-AMSAA (NHPP)#Failure_Times_-_Example_1|Failure Times - Example 1]]</noinclude><includeonly>example given above</includeonly>, calculate the 90% 2-sided confidence bounds on the cumulative and instantaneous failure intensity. | Using the values of <math>\hat{\beta }\,\!</math> and <math>\hat{\lambda }\,\!</math> estimated in the <noinclude>[[Crow-AMSAA (NHPP)#Failure_Times_-_Example_1|Failure Times - Example 1]]</noinclude><includeonly>example given above</includeonly>, calculate the 90% 2-sided confidence bounds on the cumulative and instantaneous failure intensity. | ||
'''Solution''' | '''Solution''' | ||
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The partial derivatives for the Fisher Matrix confidence bounds are: | The partial derivatives for the Fisher Matrix confidence bounds are: | ||
:<math>\begin{align} | |||
\frac{{{\partial }^{2}}\Lambda }{\partial {{\lambda }^{2}}} = & -\frac{22}{{{0.4239}^{2}}}=-122.43 \\ | \frac{{{\partial }^{2}}\Lambda }{\partial {{\lambda }^{2}}} = & -\frac{22}{{{0.4239}^{2}}}=-122.43 \\ | ||
\frac{{{\partial }^{2}}\Lambda }{\partial {{\beta }^{2}}} = & -\frac{22}{{{0.6142}^{2}}}-0.4239\cdot {{620}^{0.6142}}{{(\ln 620)}^{2}}=-967.68 \\ | \frac{{{\partial }^{2}}\Lambda }{\partial {{\beta }^{2}}} = & -\frac{22}{{{0.6142}^{2}}}-0.4239\cdot {{620}^{0.6142}}{{(\ln 620)}^{2}}=-967.68 \\ | ||
\frac{{{\partial }^{2}}\Lambda }{\partial \lambda \partial \beta } = & -{{620}^{0.6142}}\ln 620=-333.64 | \frac{{{\partial }^{2}}\Lambda }{\partial \lambda \partial \beta } = & -{{620}^{0.6142}}\ln 620=-333.64 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The Fisher Matrix then becomes: | The Fisher Matrix then becomes: | ||
:<math>\begin{align} | |||
\begin{bmatrix}122.43 & 333.64\\ 333.64 & 967.68\end{bmatrix}^{-1} & = \begin{bmatrix}Var(\hat{\lambda}) & Cov(\hat{\beta},\hat{\lambda})\\ | \begin{bmatrix}122.43 & 333.64\\ 333.64 & 967.68\end{bmatrix}^{-1} & = \begin{bmatrix}Var(\hat{\lambda}) & Cov(\hat{\beta},\hat{\lambda})\\ | ||
Cov(\hat{\beta},\hat{\lambda}) & Var(\hat{\beta})\end{bmatrix} \\ | Cov(\hat{\beta},\hat{\lambda}) & Var(\hat{\beta})\end{bmatrix} \\ | ||
& = \begin{bmatrix} 0.13519969 & -0.046614609\\ -0.046614609 & 0.017105343 \end{bmatrix} | & = \begin{bmatrix} 0.13519969 & -0.046614609\\ -0.046614609 & 0.017105343 \end{bmatrix} | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
For <math>T=620\,\!</math> hours, the partial derivatives of the cumulative and instantaneous failure intensities are: | For <math>T=620\,\!</math> hours, the partial derivatives of the cumulative and instantaneous failure intensities are: | ||
:<math>\begin{align} | |||
\frac{\partial {{\lambda }_{c}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}\ln (T) \\ | \frac{\partial {{\lambda }_{c}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}\ln (T) \\ | ||
= & 0.4239\cdot {{620}^{-0.3858}}\ln 620 \\ | = & 0.4239\cdot {{620}^{-0.3858}}\ln 620 \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | |||
\frac{\partial {{\lambda }_{i}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}+\hat{\lambda }\hat{\beta }{{T}^{\hat{\beta }-1}}\ln T \\ | \frac{\partial {{\lambda }_{i}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}+\hat{\lambda }\hat{\beta }{{T}^{\hat{\beta }-1}}\ln T \\ | ||
= & 0.4239\cdot {{620}^{-0.3858}}+0.4239\cdot 0.6142\cdot {{620}^{-0.3858}}\ln 620 \\ | = & 0.4239\cdot {{620}^{-0.3858}}+0.4239\cdot 0.6142\cdot {{620}^{-0.3858}}\ln 620 \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | |||
\frac{\partial {{\lambda }_{i}}(T)}{\partial \lambda }= & \hat{\beta }{{T}^{\hat{\beta }-1}} \\ | \frac{\partial {{\lambda }_{i}}(T)}{\partial \lambda }= & \hat{\beta }{{T}^{\hat{\beta }-1}} \\ | ||
= & 0.6142\cdot {{620}^{-0.3858}} \\ | = & 0.6142\cdot {{620}^{-0.3858}} \\ | ||
= & 0.051404969 | = & 0.051404969 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
Therefore, the variances become: | Therefore, the variances become: | ||
:<math>\begin{align} | |||
Var(\hat{\lambda_{c}}(T)) & = 0.22811336^{2}\cdot 0.017105343\ + 0.083694185^{2} \cdot 0.13519969\ -2\cdot 0.22811336\cdot 0.083694185\cdot 0.046614609 \\ | Var(\hat{\lambda_{c}}(T)) & = 0.22811336^{2}\cdot 0.017105343\ + 0.083694185^{2} \cdot 0.13519969\ -2\cdot 0.22811336\cdot 0.083694185\cdot 0.046614609 \\ | ||
& = 0.00005721408 \\ | & = 0.00005721408 \\ | ||
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&= 0.0000431393 | &= 0.0000431393 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The cumulative and instantaneous failure intensities at <math>T=620\,\!</math> hours are: | The cumulative and instantaneous failure intensities at <math>T=620\,\!</math> hours are: | ||
:<math>\begin{align} | |||
{{\lambda }_{c}}(T)= & 0.03548 \\ | {{\lambda }_{c}}(T)= & 0.03548 \\ | ||
{{\lambda }_{i}}(T)= & 0.02179 | {{\lambda }_{i}}(T)= & 0.02179 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
So, at the 90% confidence level and for <math>T=620\,\!</math> hours, the Fisher Matrix confidence bounds for the cumulative failure intensity are: | So, at the 90% confidence level and for <math>T=620\,\!</math> hours, the Fisher Matrix confidence bounds for the cumulative failure intensity are: | ||
:<math>\begin{align} | |||
{{[{{\lambda }_{c}}(T)]}_{L}}= & 0.02499 \\ | {{[{{\lambda }_{c}}(T)]}_{L}}= & 0.02499 \\ | ||
{{[{{\lambda }_{c}}(T)]}_{U}}= & 0.05039 | {{[{{\lambda }_{c}}(T)]}_{U}}= & 0.05039 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The confidence bounds for the instantaneous failure intensity are: | The confidence bounds for the instantaneous failure intensity are: | ||
:<math>\begin{align} | |||
{{[{{\lambda }_{i}}(T)]}_{L}}= & 0.01327 \\ | {{[{{\lambda }_{i}}(T)]}_{L}}= & 0.01327 \\ | ||
{{[{{\lambda }_{i}}(T)]}_{U}}= & 0.03579 | {{[{{\lambda }_{i}}(T)]}_{U}}= & 0.03579 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The following figures display plots of the Fisher Matrix confidence bounds for the cumulative and instantaneous failure intensity, respectively. | The following figures display plots of the Fisher Matrix confidence bounds for the cumulative and instantaneous failure intensity, respectively. | ||
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Given that the data is failure terminated, the Crow confidence bounds for the cumulative failure intensity at the 90% confidence level and for <math>T=620\,\!</math> hours are: | Given that the data is failure terminated, the Crow confidence bounds for the cumulative failure intensity at the 90% confidence level and for <math>T=620\,\!</math> hours are: | ||
:<math>\begin{align} | |||
{{[{{\lambda }_{c}}(T)]}_{L}} = & \frac{\chi _{\tfrac{\alpha }{2},2N}^{2}}{2\cdot t} \\ | {{[{{\lambda }_{c}}(T)]}_{L}} = & \frac{\chi _{\tfrac{\alpha }{2},2N}^{2}}{2\cdot t} \\ | ||
= & \frac{29.787476}{2*620} \\ | = & \frac{29.787476}{2*620} \\ | ||
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The Crow confidence bounds for the instantaneous failure intensity at the 90% confidence level and for <math>T=620\,\!</math> hours are calculated by first estimating the bounds on the instantaneous MTBF. Once these are calculated, take the inverse as shown below. Details on the confidence bounds for instantaneous MTBF are presented [[Crow-AMSAA Confidence Bounds#Crow_Bounds_4|here]]. | The Crow confidence bounds for the instantaneous failure intensity at the 90% confidence level and for <math>T=620\,\!</math> hours are calculated by first estimating the bounds on the instantaneous MTBF. Once these are calculated, take the inverse as shown below. Details on the confidence bounds for instantaneous MTBF are presented [[Crow-AMSAA Confidence Bounds#Crow_Bounds_4|here]]. | ||
:<math>\begin{align} | |||
{{[{{\lambda }_{i}}(t)]}_{L}} = & \frac{1}{{{[MTB{{F}_{i}}]}_{U}}} \\ | {{[{{\lambda }_{i}}(t)]}_{L}} = & \frac{1}{{{[MTB{{F}_{i}}]}_{U}}} \\ | ||
= & \frac{1}{MTB{{F}_{i}}\cdot U} \\ | = & \frac{1}{MTB{{F}_{i}}\cdot U} \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | |||
{{[{{\lambda }_{i}}(t)]}_{U}}= & \frac{1}{{{[MTB{{F}_{i}}]}_{L}}} \\ | {{[{{\lambda }_{i}}(t)]}_{U}}= & \frac{1}{{{[MTB{{F}_{i}}]}_{L}}} \\ | ||
= & \frac{1}{MTB{{F}_{i}}\cdot L} \\ | = & \frac{1}{MTB{{F}_{i}}\cdot L} \\ | ||
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From the previous example: | From the previous example: | ||
:<math>\begin{align} | |||
Var(\hat{\lambda }) = & 0.13519969 \\ | Var(\hat{\lambda }) = & 0.13519969 \\ | ||
Var(\hat{\beta }) = & 0.017105343 \\ | Var(\hat{\beta }) = & 0.017105343 \\ | ||
Cov(\hat{\beta },\hat{\lambda }) = & -0.046614609 | Cov(\hat{\beta },\hat{\lambda }) = & -0.046614609 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
And for <math>T=620\,\!</math> hours, the partial derivatives of the cumulative and instantaneous MTBF are: | And for <math>T=620\,\!</math> hours, the partial derivatives of the cumulative and instantaneous MTBF are: | ||
:<math>\begin{align} | |||
\frac{\partial {{m}_{c}}(T)}{\partial \beta }= & -\frac{1}{\hat{\lambda }}{{T}^{1-\hat{\beta }}}\ln T \\ | \frac{\partial {{m}_{c}}(T)}{\partial \beta }= & -\frac{1}{\hat{\lambda }}{{T}^{1-\hat{\beta }}}\ln T \\ | ||
= & -\frac{1}{0.4239}{{620}^{0.3858}}\ln 620 \\ | = & -\frac{1}{0.4239}{{620}^{0.3858}}\ln 620 \\ | ||
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= & -108.26001 | = & -108.26001 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
Therefore, the variances become: | Therefore, the variances become: | ||
:<math>\begin{align} | |||
Var({{\hat{m}}_{c}}(T)) = & {{\left( -181.23135 \right)}^{2}}\cdot 0.017105343+{{\left( -66.493299 \right)}^{2}}\cdot 0.13519969 \\ | Var({{\hat{m}}_{c}}(T)) = & {{\left( -181.23135 \right)}^{2}}\cdot 0.017105343+{{\left( -66.493299 \right)}^{2}}\cdot 0.13519969 \\ | ||
& -2\cdot \left( -181.23135 \right)\cdot \left( -66.493299 \right)\cdot 0.046614609 \\ | & -2\cdot \left( -181.23135 \right)\cdot \left( -66.493299 \right)\cdot 0.046614609 \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | |||
Var({{\hat{m}}_{i}}(T)) = & {{\left( -369.78634 \right)}^{2}}\cdot 0.017105343+{{\left( -108.26001 \right)}^{2}}\cdot 0.13519969 \\ | Var({{\hat{m}}_{i}}(T)) = & {{\left( -369.78634 \right)}^{2}}\cdot 0.017105343+{{\left( -108.26001 \right)}^{2}}\cdot 0.13519969 \\ | ||
& -2\cdot \left( -369.78634 \right)\cdot \left( -108.26001 \right)\cdot 0.046614609 \\ | & -2\cdot \left( -369.78634 \right)\cdot \left( -108.26001 \right)\cdot 0.046614609 \\ | ||
= & 191.33709 | = & 191.33709 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
So, at 90% confidence level and <math>T=620\,\!</math> hours, the Fisher Matrix confidence bounds are: | So, at 90% confidence level and <math>T=620\,\!</math> hours, the Fisher Matrix confidence bounds are: | ||
:<math>\begin{align} | |||
{{[{{m}_{c}}(T)]}_{L}} = & {{{\hat{m}}}_{c}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{c}}(t))}/{{{\hat{m}}}_{c}}(t)}} \\ | {{[{{m}_{c}}(T)]}_{L}} = & {{{\hat{m}}}_{c}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{c}}(t))}/{{{\hat{m}}}_{c}}(t)}} \\ | ||
= & 19.84581 \\ | = & 19.84581 \\ | ||
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:<math>\begin{align} | |||
{{[{{m}_{i}}(T)]}_{L}} = & {{{\hat{m}}}_{i}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{i}}(t))}/{{{\hat{m}}}_{i}}(t)}} \\ | {{[{{m}_{i}}(T)]}_{L}} = & {{{\hat{m}}}_{i}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{i}}(t))}/{{{\hat{m}}}_{i}}(t)}} \\ | ||
= & 27.94261 \\ | = & 27.94261 \\ | ||
| Line 211: | Line 198: | ||
The Crow confidence bounds for the cumulative MTBF and the instantaneous MTBF at the 90% confidence level and for <math>T=620\,\!</math> hours are: | The Crow confidence bounds for the cumulative MTBF and the instantaneous MTBF at the 90% confidence level and for <math>T=620\,\!</math> hours are: | ||
:<math>\begin{align} | |||
{{[{{m}_{c}}(T)]}_{L}} = & \frac{1}{{{[{{\lambda }_{c}}(T)]}_{U}}} \\ | {{[{{m}_{c}}(T)]}_{L}} = & \frac{1}{{{[{{\lambda }_{c}}(T)]}_{U}}} \\ | ||
= & 20.5023 \\ | = & 20.5023 \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
:<math>\begin{align} | |||
{{[MTB{{F}_{i}}]}_{L}} = & MTB{{F}_{i}}\cdot {{\Pi }_{1}} \\ | {{[MTB{{F}_{i}}]}_{L}} = & MTB{{F}_{i}}\cdot {{\Pi }_{1}} \\ | ||
= & 30.7445 \\ | = & 30.7445 \\ | ||
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= & 84.7972 | = & 84.7972 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
The figures below show plots of the Crow confidence bounds for the cumulative and instantaneous MTBF. | The figures below show plots of the Crow confidence bounds for the cumulative and instantaneous MTBF. | ||
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Confidence bounds can also be obtained on the parameters <math>\hat{\beta }\,\!</math> and <math>\hat{\lambda }\,\!</math>. For Fisher Matrix confidence bounds: | Confidence bounds can also be obtained on the parameters <math>\hat{\beta }\,\!</math> and <math>\hat{\lambda }\,\!</math>. For Fisher Matrix confidence bounds: | ||
:<math>\begin{align} | |||
{{\beta }_{L}} = & \hat{\beta }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\beta })}/\hat{\beta }}} \\ | {{\beta }_{L}} = & \hat{\beta }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\beta })}/\hat{\beta }}} \\ | ||
= & 0.4325 \\ | = & 0.4325 \\ | ||
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\end{align}\,\!</math> | \end{align}\,\!</math> | ||
and: | |||
:<math>\begin{align} | |||
{{\lambda }_{L}} = & \hat{\lambda }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\lambda })}/\hat{\lambda }}} \\ | {{\lambda }_{L}} = & \hat{\lambda }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\lambda })}/\hat{\lambda }}} \\ | ||
= & 0.1016 \\ | = & 0.1016 \\ | ||
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= & 1.7691 | = & 1.7691 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
For Crow confidence bounds: | For Crow confidence bounds: | ||
:<math>\begin{align} | |||
{{\beta }_{L}}= & 0.4527 \\ | {{\beta }_{L}}= & 0.4527 \\ | ||
{{\beta }_{U}}= & 0.9350 | {{\beta }_{U}}= & 0.9350 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
and: | |||
:<math>\begin{align} | |||
{{\lambda }_{L}}= & 0.2870 \\ | {{\lambda }_{L}}= & 0.2870 \\ | ||
{{\lambda }_{U}}= & 0.5827 | {{\lambda }_{U}}= & 0.5827 | ||
\end{align}\,\!</math> | \end{align}\,\!</math> | ||
Revision as of 01:18, 25 January 2014
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This example appears in the Reliability Growth and Repairable System Analysis Reference book.
Using the values of [math]\displaystyle{ \hat{\beta }\,\! }[/math] and [math]\displaystyle{ \hat{\lambda }\,\! }[/math] estimated in the Failure Times - Example 1, calculate the 90% 2-sided confidence bounds on the cumulative and instantaneous failure intensity.
Solution
Fisher Matrix Bounds
The partial derivatives for the Fisher Matrix confidence bounds are:
- [math]\displaystyle{ \begin{align} \frac{{{\partial }^{2}}\Lambda }{\partial {{\lambda }^{2}}} = & -\frac{22}{{{0.4239}^{2}}}=-122.43 \\ \frac{{{\partial }^{2}}\Lambda }{\partial {{\beta }^{2}}} = & -\frac{22}{{{0.6142}^{2}}}-0.4239\cdot {{620}^{0.6142}}{{(\ln 620)}^{2}}=-967.68 \\ \frac{{{\partial }^{2}}\Lambda }{\partial \lambda \partial \beta } = & -{{620}^{0.6142}}\ln 620=-333.64 \end{align}\,\! }[/math]
The Fisher Matrix then becomes:
- [math]\displaystyle{ \begin{align} \begin{bmatrix}122.43 & 333.64\\ 333.64 & 967.68\end{bmatrix}^{-1} & = \begin{bmatrix}Var(\hat{\lambda}) & Cov(\hat{\beta},\hat{\lambda})\\ Cov(\hat{\beta},\hat{\lambda}) & Var(\hat{\beta})\end{bmatrix} \\ & = \begin{bmatrix} 0.13519969 & -0.046614609\\ -0.046614609 & 0.017105343 \end{bmatrix} \end{align}\,\! }[/math]
For [math]\displaystyle{ T=620\,\! }[/math] hours, the partial derivatives of the cumulative and instantaneous failure intensities are:
- [math]\displaystyle{ \begin{align} \frac{\partial {{\lambda }_{c}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}\ln (T) \\ = & 0.4239\cdot {{620}^{-0.3858}}\ln 620 \\ = & 0.22811336 \\ \frac{\partial {{\lambda }_{c}}(T)}{\partial \lambda }= & {{T}^{\hat{\beta }-1}} \\ = & {{620}^{-0.3858}} \\ = & 0.083694185 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} \frac{\partial {{\lambda }_{i}}(T)}{\partial \beta }= & \hat{\lambda }{{T}^{\hat{\beta }-1}}+\hat{\lambda }\hat{\beta }{{T}^{\hat{\beta }-1}}\ln T \\ = & 0.4239\cdot {{620}^{-0.3858}}+0.4239\cdot 0.6142\cdot {{620}^{-0.3858}}\ln 620 \\ = & 0.17558519 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} \frac{\partial {{\lambda }_{i}}(T)}{\partial \lambda }= & \hat{\beta }{{T}^{\hat{\beta }-1}} \\ = & 0.6142\cdot {{620}^{-0.3858}} \\ = & 0.051404969 \end{align}\,\! }[/math]
Therefore, the variances become:
- [math]\displaystyle{ \begin{align} Var(\hat{\lambda_{c}}(T)) & = 0.22811336^{2}\cdot 0.017105343\ + 0.083694185^{2} \cdot 0.13519969\ -2\cdot 0.22811336\cdot 0.083694185\cdot 0.046614609 \\ & = 0.00005721408 \\ Var(\hat{\lambda_{i}}(T)) & = 0.17558519^{2}\cdot 0.01715343\ + 0.051404969^{2}\cdot 0.13519969\ -2\cdot 0.17558519\cdot 0.051404969\cdot 0.046614609 \\ &= 0.0000431393 \end{align}\,\! }[/math]
The cumulative and instantaneous failure intensities at [math]\displaystyle{ T=620\,\! }[/math] hours are:
- [math]\displaystyle{ \begin{align} {{\lambda }_{c}}(T)= & 0.03548 \\ {{\lambda }_{i}}(T)= & 0.02179 \end{align}\,\! }[/math]
So, at the 90% confidence level and for [math]\displaystyle{ T=620\,\! }[/math] hours, the Fisher Matrix confidence bounds for the cumulative failure intensity are:
- [math]\displaystyle{ \begin{align} {{[{{\lambda }_{c}}(T)]}_{L}}= & 0.02499 \\ {{[{{\lambda }_{c}}(T)]}_{U}}= & 0.05039 \end{align}\,\! }[/math]
The confidence bounds for the instantaneous failure intensity are:
- [math]\displaystyle{ \begin{align} {{[{{\lambda }_{i}}(T)]}_{L}}= & 0.01327 \\ {{[{{\lambda }_{i}}(T)]}_{U}}= & 0.03579 \end{align}\,\! }[/math]
The following figures display plots of the Fisher Matrix confidence bounds for the cumulative and instantaneous failure intensity, respectively.
Crow Bounds
Given that the data is failure terminated, the Crow confidence bounds for the cumulative failure intensity at the 90% confidence level and for [math]\displaystyle{ T=620\,\! }[/math] hours are:
- [math]\displaystyle{ \begin{align} {{[{{\lambda }_{c}}(T)]}_{L}} = & \frac{\chi _{\tfrac{\alpha }{2},2N}^{2}}{2\cdot t} \\ = & \frac{29.787476}{2*620} \\ = & 0.02402 \\ {{[{{\lambda }_{c}}(T)]}_{U}} = & \frac{\chi _{1-\tfrac{\alpha }{2},2N}^{2}}{2\cdot t} \\ = & \frac{60.48089}{2*620} \\ = & 0.048775 \end{align}\,\! }[/math]
The Crow confidence bounds for the instantaneous failure intensity at the 90% confidence level and for [math]\displaystyle{ T=620\,\! }[/math] hours are calculated by first estimating the bounds on the instantaneous MTBF. Once these are calculated, take the inverse as shown below. Details on the confidence bounds for instantaneous MTBF are presented here.
- [math]\displaystyle{ \begin{align} {{[{{\lambda }_{i}}(t)]}_{L}} = & \frac{1}{{{[MTB{{F}_{i}}]}_{U}}} \\ = & \frac{1}{MTB{{F}_{i}}\cdot U} \\ = & 0.01179 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} {{[{{\lambda }_{i}}(t)]}_{U}}= & \frac{1}{{{[MTB{{F}_{i}}]}_{L}}} \\ = & \frac{1}{MTB{{F}_{i}}\cdot L} \\ = & 0.03253 \end{align}\,\! }[/math]
The following figures display plots of the Crow confidence bounds for the cumulative and instantaneous failure intensity, respectively.
Failure Times - Example 3
Calculate the confidence bounds on the cumulative and instantaneous MTBF for the data from the example given above.
Solution
Fisher Matrix Bounds
From the previous example:
- [math]\displaystyle{ \begin{align} Var(\hat{\lambda }) = & 0.13519969 \\ Var(\hat{\beta }) = & 0.017105343 \\ Cov(\hat{\beta },\hat{\lambda }) = & -0.046614609 \end{align}\,\! }[/math]
And for [math]\displaystyle{ T=620\,\! }[/math] hours, the partial derivatives of the cumulative and instantaneous MTBF are:
- [math]\displaystyle{ \begin{align} \frac{\partial {{m}_{c}}(T)}{\partial \beta }= & -\frac{1}{\hat{\lambda }}{{T}^{1-\hat{\beta }}}\ln T \\ = & -\frac{1}{0.4239}{{620}^{0.3858}}\ln 620 \\ = & -181.23135 \\ \frac{\partial {{m}_{c}}(T)}{\partial \lambda } = & -\frac{1}{{{\hat{\lambda }}^{2}}}{{T}^{1-\hat{\beta }}} \\ = & -\frac{1}{{{0.4239}^{2}}}{{620}^{0.3858}} \\ = & -66.493299 \\ \frac{\partial {{m}_{i}}(T)}{\partial \beta } = & -\frac{1}{\hat{\lambda }{{\hat{\beta }}^{2}}}{{T}^{1-\beta }}-\frac{1}{\hat{\lambda }\hat{\beta }}{{T}^{1-\hat{\beta }}}\ln T \\ = & -\frac{1}{0.4239\cdot {{0.6142}^{2}}}{{620}^{0.3858}}-\frac{1}{0.4239\cdot 0.6142}{{620}^{0.3858}}\ln 620 \\ = & -369.78634 \\ \frac{\partial {{m}_{i}}(T)}{\partial \lambda } = & -\frac{1}{{{\hat{\lambda }}^{2}}\hat{\beta }}{{T}^{1-\hat{\beta }}} \\ = & -\frac{1}{{{0.4239}^{2}}\cdot 0.6142}\cdot {{620}^{0.3858}} \\ = & -108.26001 \end{align}\,\! }[/math]
Therefore, the variances become:
- [math]\displaystyle{ \begin{align} Var({{\hat{m}}_{c}}(T)) = & {{\left( -181.23135 \right)}^{2}}\cdot 0.017105343+{{\left( -66.493299 \right)}^{2}}\cdot 0.13519969 \\ & -2\cdot \left( -181.23135 \right)\cdot \left( -66.493299 \right)\cdot 0.046614609 \\ = & 36.113376 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} Var({{\hat{m}}_{i}}(T)) = & {{\left( -369.78634 \right)}^{2}}\cdot 0.017105343+{{\left( -108.26001 \right)}^{2}}\cdot 0.13519969 \\ & -2\cdot \left( -369.78634 \right)\cdot \left( -108.26001 \right)\cdot 0.046614609 \\ = & 191.33709 \end{align}\,\! }[/math]
So, at 90% confidence level and [math]\displaystyle{ T=620\,\! }[/math] hours, the Fisher Matrix confidence bounds are:
- [math]\displaystyle{ \begin{align} {{[{{m}_{c}}(T)]}_{L}} = & {{{\hat{m}}}_{c}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{c}}(t))}/{{{\hat{m}}}_{c}}(t)}} \\ = & 19.84581 \\ {{[{{m}_{c}}(T)]}_{U}} = & {{{\hat{m}}}_{c}}(t){{e}^{{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{c}}(t))}/{{{\hat{m}}}_{c}}(t)}} \\ = & 40.01927 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} {{[{{m}_{i}}(T)]}_{L}} = & {{{\hat{m}}}_{i}}(t){{e}^{-{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{i}}(t))}/{{{\hat{m}}}_{i}}(t)}} \\ = & 27.94261 \\ {{[{{m}_{i}}(T)]}_{U}} = & {{{\hat{m}}}_{i}}(t){{e}^{{{z}_{\alpha }}\sqrt{Var({{{\hat{m}}}_{i}}(t))}/{{{\hat{m}}}_{i}}(t)}} \\ = & 75.34193 \end{align}\,\! }[/math]
The following two figures show plots of the Fisher Matrix confidence bounds for the cumulative and instantaneous MTBFs.
Crow Bounds
The Crow confidence bounds for the cumulative MTBF and the instantaneous MTBF at the 90% confidence level and for [math]\displaystyle{ T=620\,\! }[/math] hours are:
- [math]\displaystyle{ \begin{align} {{[{{m}_{c}}(T)]}_{L}} = & \frac{1}{{{[{{\lambda }_{c}}(T)]}_{U}}} \\ = & 20.5023 \\ {{[{{m}_{c}}(T)]}_{U}} = & \frac{1}{{{[{{\lambda }_{c}}(T)]}_{L}}} \\ = & 41.6282 \end{align}\,\! }[/math]
- [math]\displaystyle{ \begin{align} {{[MTB{{F}_{i}}]}_{L}} = & MTB{{F}_{i}}\cdot {{\Pi }_{1}} \\ = & 30.7445 \\ {{[MTB{{F}_{i}}]}_{U}} = & MTB{{F}_{i}}\cdot {{\Pi }_{2}} \\ = & 84.7972 \end{align}\,\! }[/math]
The figures below show plots of the Crow confidence bounds for the cumulative and instantaneous MTBF.
Confidence bounds can also be obtained on the parameters [math]\displaystyle{ \hat{\beta }\,\! }[/math] and [math]\displaystyle{ \hat{\lambda }\,\! }[/math]. For Fisher Matrix confidence bounds:
- [math]\displaystyle{ \begin{align} {{\beta }_{L}} = & \hat{\beta }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\beta })}/\hat{\beta }}} \\ = & 0.4325 \\ {{\beta }_{U}} = & \hat{\beta }{{e}^{-{{z}_{\alpha }}\sqrt{Var(\hat{\beta })}/\hat{\beta }}} \\ = & 0.8722 \end{align}\,\! }[/math]
and:
- [math]\displaystyle{ \begin{align} {{\lambda }_{L}} = & \hat{\lambda }{{e}^{{{z}_{\alpha }}\sqrt{Var(\hat{\lambda })}/\hat{\lambda }}} \\ = & 0.1016 \\ {{\lambda }_{U}} = & \hat{\lambda }{{e}^{-{{z}_{\alpha }}\sqrt{Var(\hat{\lambda })}/\hat{\lambda }}} \\ = & 1.7691 \end{align}\,\! }[/math]
For Crow confidence bounds:
- [math]\displaystyle{ \begin{align} {{\beta }_{L}}= & 0.4527 \\ {{\beta }_{U}}= & 0.9350 \end{align}\,\! }[/math]
and:
- [math]\displaystyle{ \begin{align} {{\lambda }_{L}}= & 0.2870 \\ {{\lambda }_{U}}= & 0.5827 \end{align}\,\! }[/math]