Difference between revisions of "Template:Grp model"

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::<span class="texhtml">λ(''t'') = λβ''t''<sup>β − 1</sup></span>
::<span class="texhtml">λ(''t'') = λβ''t''<sup>β − 1</sup></span>
The conditional <span class="texhtml">''p''''d''''f''</span> is:  
The conditional <span class="texhtml">''pdf''</span> is:  
::<math>f({{t}_{i}}|{{t}_{i-1}})=\lambda \beta {{({{x}_{i}}+{{v}_{i-1}})}^{\beta -1}}{{e}^{-\lambda \left[ {{\left( {{x}_{i}}+{{v}_{i-1}} \right)}^{\beta }}-v_{i-1}^{\beta } \right]}}</math>
::<math>f({{t}_{i}}|{{t}_{i-1}})=\lambda \beta {{({{x}_{i}}+{{v}_{i-1}})}^{\beta -1}}{{e}^{-\lambda \left[ {{\left( {{x}_{i}}+{{v}_{i-1}} \right)}^{\beta }}-v_{i-1}^{\beta } \right]}}</math>

Revision as of 17:25, 8 March 2012

The GRP Model

In this model, the concept of virtual age is introduced. Let [math]{{t}_{1}},{{t}_{2}},\cdots ,{{t}_{n}}[/math] represent the successive failure times and let [math]{{x}_{1}},{{x}_{2}},\cdots ,{{x}_{n}}[/math] represent the time between failures ( [math]{{t}_{i}}=\sum_{j=1}^{i}{{x}_{j}})[/math] . Assume that after each event, actions are taken to improve the system performance. Let q be the action effectiveness factor. There are two GRP models:

Type I:

vi = vi − 1 + q''x = qti

Type II:

[math]{{v}_{i}}=q({{v}_{i-1}}+{{x}_{i}})={{q}^{i}}{{x}_{1}}+{{q}^{i-1}}{{x}_{2}}+\cdots +{{x}_{i}}[/math]

where vi is the virtual age of the system right after i th repair. The Type I model assumes that the i th repair cannot remove the damage incurred before the ith failure. It can only reduce the additional age xi to q'xi . The Type II model assumes that at the i th repair, the virtual age has been accumulated to vi − 1 + xi . The i th repair will remove the cumulative damage from both current and previous failures by reducing the virtual age to q(vi − 1 + xi) .

The power law function is used to model the rate of recurrence, which is:

λ(t) = λβtβ − 1

The conditional pdf is:

[math]f({{t}_{i}}|{{t}_{i-1}})=\lambda \beta {{({{x}_{i}}+{{v}_{i-1}})}^{\beta -1}}{{e}^{-\lambda \left[ {{\left( {{x}_{i}}+{{v}_{i-1}} \right)}^{\beta }}-v_{i-1}^{\beta } \right]}}[/math]

MLE method is used to estimate the model parameters. The log likelihood function is [28]:

[math]\begin{align} & \ln (L)= n(\ln \lambda +\ln \beta )-\lambda \left[ {{\left( T-{{t}_{n}}+{{v}_{n}} \right)}^{\beta }}-v_{n}^{\beta } \right] \\ & -\lambda \underset{i=1}{\overset{n}{\mathop \sum }}\,\left[ {{\left( {{x}_{i}}+{{v}_{i-1}} \right)}^{\beta }}-v_{i}^{\beta } \right]+(\beta -1)\underset{i=1}{\overset{n}{\mathop \sum }}\,\ln ({{x}_{i}}+{{v}_{i-1}}) \end{align}[/math]

where n is the total number of events during the entire observation period. T is the stop time of the observation. T = tn if the observation stops right after the last event.