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Equation (8) indicates the integrated impact of a specified maintenance policy on the system reliability: {text{Fun}} = T_{text{mis}}^{ - 1} mathop int limits_{0}^{{T_{text{mis}} }} R_{text{sis}} {text{d}}t.
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The model can be manipulated under different set of parameter values to see the impact of various maintenance policies on system availability.
Although a lot of maintenance models have been created during the past decades, there are few maintenance policies on which all the other maintenance models can be based (Wang 2002).
To demonstrate the model, a system replacement maintenance policy based on a replacement interval variable is applied to an example system to obtain the optimal replacement time.
According to Shyjith et al. (2008), selecting a maintenance policy based on a few factors makes it unrealistic.
In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations).
In addition to the theoretical development of Kijima models and its extensions, the review highlights their applications such as designing maintenance policies based on the concept of ARA.
Assessment (iii): This is a macro activity in which a hierarchical model, comprising RBD and SPN models, is used to evaluate the impact that different redundant mechanisms and maintenance policies have on the steady-state availability and reliability of an environment.
The stock level of spare parts is dependent on the maintenance policy.
The operational efficiency of equipment units depends strongly on the maintenance policy employed.
A numerical example of the described methodology is presented based on a chosen maintenance policy.
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