Exponential Distribution Functions: Difference between revisions
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''This article also appears in the [https://help.reliasoft.com/reference/life_data_analysis Life data analysis reference] and [https://help.reliasoft.com/reference/accelerated_life_testing_data_analysis Accelerated life testing reference].'' </noinclude> | |||
===The Mean or MTTF=== | ===The Mean or MTTF=== | ||
The mean, <math>\overline{T},</math> or mean time to failure (MTTF) is given by: | The mean, <math>\overline{T},\,\!</math> or mean time to failure (MTTF) is given by: | ||
::<math>\begin{align} | ::<math>\begin{align} | ||
| Line 7: | Line 8: | ||
= & \int_{\gamma }^{\infty }t\cdot \lambda \cdot {{e}^{-\lambda t}}dt \\ | = & \int_{\gamma }^{\infty }t\cdot \lambda \cdot {{e}^{-\lambda t}}dt \\ | ||
= & \gamma +\frac{1}{\lambda }=m | = & \gamma +\frac{1}{\lambda }=m | ||
\end{align}</math> | \end{align}\,\!</math> | ||
Note that when <math>\gamma =0\,\!</math>, the MTTF is the inverse of the exponential distribution's constant failure rate. This is only true for the exponential distribution. Most other distributions do not have a constant failure rate. Consequently, the inverse relationship between failure rate and MTTF does not hold for these other distributions. | |||
Note that when <math>\gamma =0</math>, the MTTF is the inverse of the exponential distribution's constant failure rate. This is only true for the exponential distribution. Most other distributions do not have a constant failure rate. Consequently, the inverse relationship between failure rate and MTTF does not hold for these other distributions. | |||
===The Median=== | ===The Median=== | ||
The median, <math> \breve{T}, </math> is: | The median, <math> \breve{T}, \,\!</math> is: | ||
::<math> \breve{T}=\gamma +\frac{1}{\lambda}\cdot 0.693 \,\!</math> | |||
===The Mode=== | ===The Mode=== | ||
The mode, <math>\tilde{T},</math> is: | The mode, <math>\tilde{T},\,\!</math> is: | ||
::<math>\tilde{T}=\gamma </math> | ::<math>\tilde{T}=\gamma \,\!</math> | ||
===The Standard Deviation=== | ===The Standard Deviation=== | ||
The standard deviation, <math>{\sigma }_{T}</math>, is: | The standard deviation, <math>{\sigma }_{T}\,\!</math>, is: | ||
::<math>{\sigma}_{T}=\frac{1}{\lambda }=m</math> | ::<math>{\sigma}_{T}=\frac{1}{\lambda }=m\,\!</math> | ||
===The Exponential Reliability Function=== | ===The Exponential Reliability Function=== | ||
The equation for the 2-parameter exponential cumulative density function, or | The equation for the 2-parameter exponential cumulative density function, or ''cdf'', is given by: | ||
::<math>\begin{align} | ::<math>\begin{align} | ||
F(t)=Q(t)=1-{{e}^{-\lambda (t-\gamma )}} | F(t)=Q(t)=1-{{e}^{-\lambda (t-\gamma )}} | ||
\end{align}</math> | \end{align}\,\!</math> | ||
Recalling that the reliability function of a distribution is simply one minus the | Recalling that the reliability function of a distribution is simply one minus the ''cdf'', the reliability function of the 2-parameter exponential distribution is given by: | ||
::<math>R(t)=1-Q(t)=1-\int_{0}^{t-\gamma }f(x)dx</math> | ::<math>R(t)=1-Q(t)=1-\int_{0}^{t-\gamma }f(x)dx\,\!</math> | ||
::<math>R(t)=1-\int_{0}^{t-\gamma }\lambda {{e}^{-\lambda x}}dx={{e}^{-\lambda (t-\gamma )}}</math> | ::<math>R(t)=1-\int_{0}^{t-\gamma }\lambda {{e}^{-\lambda x}}dx={{e}^{-\lambda (t-\gamma )}}\,\!</math> | ||
The 1-parameter exponential reliability function is given by: | The 1-parameter exponential reliability function is given by: | ||
::<math>R(t)={{e}^{-\lambda t}}={{e}^{-\tfrac{t}{m}}}</math> | ::<math>R(t)={{e}^{-\lambda t}}={{e}^{-\tfrac{t}{m}}}\,\!</math> | ||
===The Exponential Conditional Reliability Function=== | ===The Exponential Conditional Reliability Function=== | ||
The exponential conditional reliability equation gives the reliability for a mission of <math>t</math> duration, having already successfully accumulated <math>T</math> hours of operation up to the start of this new mission. The exponential conditional reliability function is: | The exponential conditional reliability equation gives the reliability for a mission of <math>t\,\!</math> duration, having already successfully accumulated <math>T\,\!</math> hours of operation up to the start of this new mission. The exponential conditional reliability function is: | ||
::<math>R(t|T)=\frac{R(T+t)}{R(T)}=\frac{{{e}^{-\lambda (T+t-\gamma )}}}{{{e}^{-\lambda (T-\gamma )}}}={{e}^{-\lambda t}}</math> | ::<math>R(t|T)=\frac{R(T+t)}{R(T)}=\frac{{{e}^{-\lambda (T+t-\gamma )}}}{{{e}^{-\lambda (T-\gamma )}}}={{e}^{-\lambda t}}\,\!</math> | ||
which says that the reliability for a mission of <math>t</math> duration undertaken after the component or equipment has already accumulated <math>T</math> hours of operation from age zero is only a function of the mission duration, and not a function of the age at the beginning of the mission. This is referred to as the memoryless property. | which says that the reliability for a mission of <math>t\,\!</math> duration undertaken after the component or equipment has already accumulated <math>T\,\!</math> hours of operation from age zero is only a function of the mission duration, and not a function of the age at the beginning of the mission. This is referred to as the ''memoryless property''. | ||
===The Exponential Reliable Life=== | ===The Exponential Reliable Life Function=== | ||
The reliable life, or the mission duration for a desired reliability goal, <math>{{t}_{R}}</math>, for the | The reliable life, or the mission duration for a desired reliability goal, <math>{{t}_{R}}\,\!</math>, for the 1-parameter exponential distribution is: | ||
::<math>R({{t}_{R}})={{e}^{-\lambda ({{t}_{R}}-\gamma )}}</math> | ::<math>R({{t}_{R}})={{e}^{-\lambda ({{t}_{R}}-\gamma )}}\,\!</math> | ||
::<math>\begin{align} | ::<math>\begin{align} | ||
\ln[R({{t}_{R}})]=-\lambda({{t}_{R}}-\gamma ) | \ln[R({{t}_{R}})]=-\lambda({{t}_{R}}-\gamma ) | ||
\end{align}</math> | \end{align}\,\!</math> | ||
or: | or: | ||
::<math>{{t}_{R}}=\gamma -\frac{\ln [R({{t}_{R}})]}{\lambda }</math> | ::<math>{{t}_{R}}=\gamma -\frac{\ln [R({{t}_{R}})]}{\lambda }\,\!</math> | ||
===The Exponential Failure Rate Function=== | ===The Exponential Failure Rate Function=== | ||
The exponential failure rate function is: | The exponential failure rate function is: | ||
::<math>\lambda (t)=\frac{f(t)}{R(t)}=\frac{\lambda {{e}^{-\lambda (t-\gamma )}}}{{{e}^{-\lambda (t-\gamma )}}}=\lambda =\text{constant}</math> | ::<math>\lambda (t)=\frac{f(t)}{R(t)}=\frac{\lambda {{e}^{-\lambda (t-\gamma )}}}{{{e}^{-\lambda (t-\gamma )}}}=\lambda =\text{constant}\,\!</math> | ||
Once again, note that the constant failure rate is a characteristic of the exponential distribution, and special cases of other distributions only. Most other distributions have failure rates that are functions of time. | Once again, note that the constant failure rate is a characteristic of the exponential distribution, and special cases of other distributions only. Most other distributions have failure rates that are functions of time. | ||
Latest revision as of 21:41, 18 September 2023
This article also appears in the Life data analysis reference and Accelerated life testing reference.
The Mean or MTTF
The mean, [math]\displaystyle{ \overline{T},\,\! }[/math] or mean time to failure (MTTF) is given by:
- [math]\displaystyle{ \begin{align} \bar{T}= & \int_{\gamma }^{\infty }t\cdot f(t)dt \\ = & \int_{\gamma }^{\infty }t\cdot \lambda \cdot {{e}^{-\lambda t}}dt \\ = & \gamma +\frac{1}{\lambda }=m \end{align}\,\! }[/math]
Note that when [math]\displaystyle{ \gamma =0\,\! }[/math], the MTTF is the inverse of the exponential distribution's constant failure rate. This is only true for the exponential distribution. Most other distributions do not have a constant failure rate. Consequently, the inverse relationship between failure rate and MTTF does not hold for these other distributions.
The Median
The median, [math]\displaystyle{ \breve{T}, \,\! }[/math] is:
- [math]\displaystyle{ \breve{T}=\gamma +\frac{1}{\lambda}\cdot 0.693 \,\! }[/math]
The Mode
The mode, [math]\displaystyle{ \tilde{T},\,\! }[/math] is:
- [math]\displaystyle{ \tilde{T}=\gamma \,\! }[/math]
The Standard Deviation
The standard deviation, [math]\displaystyle{ {\sigma }_{T}\,\! }[/math], is:
- [math]\displaystyle{ {\sigma}_{T}=\frac{1}{\lambda }=m\,\! }[/math]
The Exponential Reliability Function
The equation for the 2-parameter exponential cumulative density function, or cdf, is given by:
- [math]\displaystyle{ \begin{align} F(t)=Q(t)=1-{{e}^{-\lambda (t-\gamma )}} \end{align}\,\! }[/math]
Recalling that the reliability function of a distribution is simply one minus the cdf, the reliability function of the 2-parameter exponential distribution is given by:
- [math]\displaystyle{ R(t)=1-Q(t)=1-\int_{0}^{t-\gamma }f(x)dx\,\! }[/math]
- [math]\displaystyle{ R(t)=1-\int_{0}^{t-\gamma }\lambda {{e}^{-\lambda x}}dx={{e}^{-\lambda (t-\gamma )}}\,\! }[/math]
The 1-parameter exponential reliability function is given by:
- [math]\displaystyle{ R(t)={{e}^{-\lambda t}}={{e}^{-\tfrac{t}{m}}}\,\! }[/math]
The Exponential Conditional Reliability Function
The exponential conditional reliability equation gives the reliability for a mission of [math]\displaystyle{ t\,\! }[/math] duration, having already successfully accumulated [math]\displaystyle{ T\,\! }[/math] hours of operation up to the start of this new mission. The exponential conditional reliability function is:
- [math]\displaystyle{ R(t|T)=\frac{R(T+t)}{R(T)}=\frac{{{e}^{-\lambda (T+t-\gamma )}}}{{{e}^{-\lambda (T-\gamma )}}}={{e}^{-\lambda t}}\,\! }[/math]
which says that the reliability for a mission of [math]\displaystyle{ t\,\! }[/math] duration undertaken after the component or equipment has already accumulated [math]\displaystyle{ T\,\! }[/math] hours of operation from age zero is only a function of the mission duration, and not a function of the age at the beginning of the mission. This is referred to as the memoryless property.
The Exponential Reliable Life Function
The reliable life, or the mission duration for a desired reliability goal, [math]\displaystyle{ {{t}_{R}}\,\! }[/math], for the 1-parameter exponential distribution is:
- [math]\displaystyle{ R({{t}_{R}})={{e}^{-\lambda ({{t}_{R}}-\gamma )}}\,\! }[/math]
- [math]\displaystyle{ \begin{align} \ln[R({{t}_{R}})]=-\lambda({{t}_{R}}-\gamma ) \end{align}\,\! }[/math]
or:
- [math]\displaystyle{ {{t}_{R}}=\gamma -\frac{\ln [R({{t}_{R}})]}{\lambda }\,\! }[/math]
The Exponential Failure Rate Function
The exponential failure rate function is:
- [math]\displaystyle{ \lambda (t)=\frac{f(t)}{R(t)}=\frac{\lambda {{e}^{-\lambda (t-\gamma )}}}{{{e}^{-\lambda (t-\gamma )}}}=\lambda =\text{constant}\,\! }[/math]
Once again, note that the constant failure rate is a characteristic of the exponential distribution, and special cases of other distributions only. Most other distributions have failure rates that are functions of time.