Arrhenius Relationship

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).

Introduction

The Arrhenius life-stress model (or relationship) is probably the most common life-stress relationship utilized in accelerated life testing. It has been widely used when the stimulus or acceleration variable (or stress) is thermal (i.e., temperature). It is derived from the Arrhenius reaction rate equation proposed by the Swedish physical chemist Svandte Arrhenius in 1887.

Life Stress Plots

The Arrhenius relationship can be linearized and plotted on a Life vs. Stress plot, also called the Arrhenius plot. The relationship is linearized by taking the natural logarithm of both sides in the Arrhenius equation or:

[math]\displaystyle{ ln(L(V))=ln(C)+\frac{B}{V} }[/math]

[math]\displaystyle{ }[/math]

Arrhenius plot for Weibull life distribution.

In the linearized Arrhenius equation, [math]\displaystyle{ \ln (C) }[/math] is the intercept of the line and [math]\displaystyle{ B }[/math] is the slope of the line. Note that the inverse of the stress, and not the stress, is the variable. In the above figure, life is plotted versus stress and not versus the inverse stress. This is because the linearized Arrhenius equation was plotted on a reciprocal scale. On such a scale, the slope [math]\displaystyle{ B }[/math] appears to be negative even though it has a positive value. This is because [math]\displaystyle{ B }[/math] is actually the slope of the reciprocal of the stress and not the slope of the stress. The reciprocal of the stress is decreasing as stress is increasing ( [math]\displaystyle{ \tfrac{1}{V} }[/math] is decreasing as [math]\displaystyle{ V }[/math] is increasing). The two different axes are shown in the next figure.

[math]\displaystyle{ }[/math]

An illustration of both reciprocal and non-reciprocal scales.

The Arrhenius relationship is plotted on a reciprocal scale for practical reasons. For example, in the above figure it is more convenient to locate the life corresponding to a stress level of 370K than to take the reciprocal of 370K (0.0027) first, and then locate the corresponding life. The shaded areas shown in the above figure are the imposed at each test stress level. From such imposed [math]\displaystyle{ pdfs }[/math] one can see the range of the life at each test stress level, as well as the scatter in life. The next figure illustrates a case in which there is a significant scatter in life at each of the test stress levels.

[math]\displaystyle{ }[/math]

An example of scatter in life at each test stress level.

Activation Energy and the Parameter B

Depending on the application (and where the stress is exclusively thermal), the parameter [math]\displaystyle{ B }[/math] can be replaced by:

[math]\displaystyle{ B=-\frac{{{E}_{A}}}{K}=-\frac{\text{activation energy}}{\text{Boltzma}{{\text{n}}^{\prime }}\text{s constant}}=-\frac{\text{activation energy}}{8.623\times {{10}^{-5}}\text{eV}{{\text{K}}^{-1}}} }[/math]

Note that in this formulation, the activation energy [math]\displaystyle{ {{E}_{A}} }[/math] must be known a priori. If the activation energy is known then there is only one model parameter remaining, [math]\displaystyle{ C. }[/math] Because in most real life situations this is rarely the case, all subsequent formulations will assume that this activation energy is unknown and treat [math]\displaystyle{ B }[/math] as one of the model parameters. [math]\displaystyle{ B }[/math] has the same properties as the activation energy. In other words, [math]\displaystyle{ B }[/math] is a measure of the effect that the stress (i.e. temperature) has on the life. The larger the value of [math]\displaystyle{ B, }[/math] the higher the dependency of the life on the specific stress. Parameter [math]\displaystyle{ B }[/math] may also take negative values. In that case, life is increasing with increasing stress. An example of this would be plasma filled bulbs, where low temperature is a higher stress on the bulbs than high temperature.

[math]\displaystyle{ }[/math]

Behavior of the parameter B.

Consider the following times-to-failure data at three different stress levels.

The data set was analyzed jointly and with a complete MLE solution over the entire data set, using ReliaSoft's ALTA. The analysis yields:

[math]\displaystyle{ \widehat{\beta }=4.2915822\,\! }[/math]

[math]\displaystyle{ \widehat{B}=1861.6186657\,\! }[/math]

[math]\displaystyle{ \widehat{C}=58.9848692\,\! }[/math]

Once the parameters of the model are estimated, extrapolation and other life measures can be directly obtained using the appropriate equations. Using the MLE method, confidence bounds for all estimates can be obtained. Note that in the next figure, the more distant the accelerated stress is from the operating stress, the greater the uncertainty of the extrapolation. The degree of uncertainty is reflected in the confidence bounds. (General theory and calculations for confidence intervals are presented in Appendix A. Specific calculations for confidence bounds on the Arrhenius model are presented in the Arrhenius Relationship chapter).