Template:Gll relationship

General Log-Linear Relationship
When a test involves multiple accelerating stresses or requires the inclusion of an engineering variable, a general multivariable relationship is needed. Such a relationship is the general log-linear relationship, which describes a life characteristic as a function of a vector of $$n$$  stresses, or  $$\underline{X}=({{X}_{1}},{{X}_{2}}...{{X}_{n}}).$$  ALTA includes this relationship and allows up to eight stresses. Mathematically the relationship is given by:
 * $$L(\underline{X})={{e}^{{{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}}}}$$


 * where:

•	 $${{\alpha }_{0}}$$ and  $${{\alpha }_{j}}$$  are model parameters.

•	 $$X$$ is a vector of  $$n$$  stresses.

This relationship can be further modified through the use of transformations and can be reduced to the relationships discussed previously, if so desired. As an example, consider a single stress application of this relationship and an inverse transformation on $$X,$$  such that  $$V=1/X$$  or:


 * $$\begin{align}

& L(V)= & {{e}^{{{\alpha }_{0}}+\tfrac{V}}} \\ & = & {{e}^}{{e}^{\tfrac{V}}} \end{align}$$

It can be easily seen that the generalized log-linear relationship with a single stress and an inverse transformation, Eqn. (argll), has been reduced to the Arrhenius relationship discussed in Chapter 6, where:
 * $$\begin{align}

& C= & {{e}^} \\ & B= & {{\alpha }_{1}} \end{align}$$


 * or:


 * $$L(V)=C{{e}^{\tfrac{B}{V}}}$$

Similarly, when one chooses to apply a logarithmic transformation on $$X$$  such that  $$V=\ln (X)$$, the relationship would reduce to the inverse power relation discussed in Chapter 8. Furthermore, if more than one stress is present, one could choose to apply a different transformation to each stress to create combination relationships similar to the ones discussed in Chapters 9 and 10. ALTA PRO has three built-in transformation options, namely:

The power of the relationship and this formulation becomes evident once one realizes that 6,651 unique life-stress relationships are possible (when allowing a maximum of eight stresses). When combined with the life distributions available in ALTA PRO, almost 20,000 models can be created.

Using the GLL Model
Like the previous relationships, the general log-linear relationship can be combined with any of the available life distributions by expressing a life characteristic from that distribution with the GLL relationship. A brief overview of the GLL-distribution models available in ALTA PRO is presented next.

GLL Likelihood Function
The maximum likelihood estimation method can be used to determine the parameters for the GLL relationship and the selected life distribution. For each distribution, the likelihood function can be derived, and the parameters of model (the distribution parameters and the GLL parameters) can be obtained by maximizing the log-likelihood function. For example, the log-likelihood function for the Weibull distribution is given by:


 * $$\begin{align}

& \ln (L)= & \Lambda =\underset{i=1}{\overset{\mathop \sum }}\,{{N}_{i}}\ln \left[ \beta \cdot T_{i}^{\beta -1}{{e}^{-T_{i}^{\beta }\cdot {{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}}}}{{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}} \right] \\ & & -\underset{i=1}{\overset{S}{\mathop \sum }}\,N_{i}^{\prime }{{\left( T_{i}^{\prime } \right)}^{\beta }}{{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}}+\overset{FI}{\mathop{\underset{i=1}{\mathop{\underset{}{\overset{}{\mathop \sum }}\,}}\,}}\,N_{i}^{\prime \prime }\ln [R_{Li}^{\prime \prime }-R_{Ri}^{\prime \prime }] \end{align}$$


 * where:


 * $$\begin{align}

& R_{Li}^{\prime \prime }= & {{e}^{-{{\left( T_{Li}^{\prime \prime }{{e}^{{{\alpha }_{0}}+\underset{j=1}{\mathop{\overset{n}{\mathop{\mathop{}_{}^{}}}\,}}\,{{\alpha }_{j}}{{x}_{j}}}} \right)}^{\beta }}}} \\ & R_{Ri}^{\prime \prime }= & {{e}^{-{{\left( T_{Ri}^{\prime \prime }{{e}^{{{\alpha }_{0}}+\underset{j=1}{\mathop{\overset{n}{\mathop{\mathop{}_{}^{}}}\,}}\,{{\alpha }_{j}}{{x}_{j}}}} \right)}^{\beta }}}} \end{align}$$


 * and:

•	 $${{F}_{e}}$$ is the number of groups of exact times-to-failure data points. •	 $${{N}_{i}}$$ is the number of times-to-failure in the  $${{i}^{th}}$$  time-to-failure data group. •	 $$\lambda $$ is the failure rate parameter (unknown). •	 $${{T}_{i}}$$ is the exact failure time of the  $${{i}^{th}}$$  group. •	 $$S$$ is the number of groups of suspension data points. •	 $$N_{i}^{\prime }$$ is the number of suspensions in the  $${{i}^{th}}$$  group of suspension data points. •	 $$T_{i}^{\prime }$$ is the running time of the  $${{i}^{th}}$$  suspension data group. •	 $$FI$$ is the number of interval data groups. •	 $$N_{i}^{\prime \prime }$$ is the number of intervals in the  $${{i}^{th}}$$  group of data intervals. •	 $$T_{Li}^{\prime \prime }$$ is the beginning of the  $${{i}^{th}}$$  interval. •	 $$T_{Ri}^{\prime \prime }$$ is the ending of the  $${{i}^{th}}$$  interval.