Template:Grp model

The GRP Model

In this model, the concept of virtual age is introduced. Let [math]\displaystyle{ {{t}_{1}},{{t}_{2}},\cdots ,{{t}_{n}} }[/math] represent the successive failure times and let [math]\displaystyle{ {{x}_{1}},{{x}_{2}},\cdots ,{{x}_{n}} }[/math] represent the time between failures ( [math]\displaystyle{ {{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:

v_i = v_{i − 1} + q''x = qt_i

Type II:

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

where v_i 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 x_i to q'x_i . The Type II model assumes that at the i th repair, the virtual age has been accumulated to v_{i − 1} + x_i . The i th repair will remove the cumulative damage from both current and previous failures by reducing the virtual age to q(v_{i − 1} + x_i) .

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

λ(t) = λβt^{β − 1}

The conditional pdf is:

[math]\displaystyle{ 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]\displaystyle{ \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 = t_n if the observation stops right after the last event.