Abstract
In maintenance management process of aircraft, how to use the lesser maintenance manpower to get in return the higher maintenance Benefits, it is a problem that the management specialists pay more attention to, so, manpower management and cost estimate are the important compose parts of aircraft Life Cycle Costs (LCC) management. Via improve the maintenance level of aircraft, predigesting maintenance system from three level to two level maintenance (TLM) could save manpower, improve readiness, achieve the goal of bring favorable maintenance management Benefits. We imported gray system Verhulst model theory, established the manpower management forecast model for transforming maintenance system, and predicted according to the actual save manpower data of the United State Air Force. It is proved that the creditability of the prediction model is higher, has certain practical value.
Highlights
Basic conception of gray prediction theoryThe existing system statistical analysis usually use the mathematics statistics method, and the sample are the more and the better, distribution rule is known, this method isn’t fit the condition of unknown distribution rule
In maintenance management process of aircraft, how to use the lesser maintenance manpower to get in return the higher maintenance Benefits, it is a problem that the management specialists pay more attention to, so, manpower management and cost estimate are the important compose parts of aircraft Life Cycle Costs (LCC) management
Partial information is known and partial information is unknown, we called such system as gray system. This system regards the random variable as the gray variable which is varying in a certain range, regards the random process as the gray process which is varying in a certain range and a certain time period, use the original data to accumulating generator operator (AGO), and attenuate it’s random factors, establish the whitenization form differential equation for the making sequence of numbers, find out the equation’s solution sequence of numbers, through the inverse accumulating generator operator (IAGO), we find out the predicted value
Summary
The existing system statistical analysis usually use the mathematics statistics method, and the sample are the more and the better, distribution rule is known, this method isn’t fit the condition of unknown distribution rule. Partial information is known and partial information is unknown, we called such system as gray system This system regards the random variable as the gray variable which is varying in a certain range, regards the random process as the gray process which is varying in a certain range and a certain time period, use the original data to accumulating generator operator (AGO), and attenuate it’s random factors, establish the whitenization form differential equation for the making sequence of numbers, find out the equation’s solution sequence of numbers, through the inverse accumulating generator operator (IAGO), we find out the predicted value. (1) Though the grey prediction, we can establish the differential equation This equation is a differential equation type model, which established by the viewpoints and methods of relational degree convergence theory, generator number, and grey differential equation, it has a wide range of use. (5) Because grey prediction establishes model is a generator data model, the results calculate value by the GM model must be dealt with inverse generator to revert prediction value
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