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(Created page with '====Arrhenius-Weibull Reliability Function==== <br> The Arrhenius-Weibull reliability function is given by: <br> ::<math>R(T,V)={{e}^{-{{\left( \tfrac{T}{C\cdot {{e}^{\tfrac{B}{…')
 
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The behavior of the reliability function of the Weibull distribution for different values of  <math>\beta </math>  was illustrated in Chapter 5. In the case of the Arrhenius-Weibull model, however, the reliability is a function of stress also. A 3D plot such as the ones shown in Fig. 8 is now needed to illustrate the effects of both the stress and  <math>\beta .</math>  
The behavior of the reliability function of the Weibull distribution for different values of  <math>\beta </math>  was illustrated [[Distributions used in Accelerated Testing#The Weibull Distribution|here]]. In the case of the Arrhenius-Weibull model, however, the reliability is a function of stress also. A 3D plot such as the ones shown in the next figure is now needed to illustrate the effects of both the stress and  <math>\beta .</math>  


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Revision as of 23:30, 13 February 2012

Arrhenius-Weibull Reliability Function


The Arrhenius-Weibull reliability function is given by:


[math]\displaystyle{ R(T,V)={{e}^{-{{\left( \tfrac{T}{C\cdot {{e}^{\tfrac{B}{V}}}} \right)}^{\beta }}}} }[/math]


If the parameter [math]\displaystyle{ B }[/math] is positive, then the reliability increases as stress decreases.

Behavior of the reliability function at different stress and constant parameter values.



The behavior of the reliability function of the Weibull distribution for different values of [math]\displaystyle{ \beta }[/math] was illustrated here. In the case of the Arrhenius-Weibull model, however, the reliability is a function of stress also. A 3D plot such as the ones shown in the next figure is now needed to illustrate the effects of both the stress and [math]\displaystyle{ \beta . }[/math]


[math]\displaystyle{ }[/math]


Reliability function for [math]\displaystyle{ \Beta\lt 1 }[/math], [math]\displaystyle{ \Beta=1 }[/math], and [math]\displaystyle{ \Beta\gt 1 }[/math].