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The evaluation of maintainability growth plays an important role in improving materiel system effectiveness. Based on field maintenance information, a maximum likelihood model is put forward to evaluate the maintainability growth at the system level and sub-system level. A maximum likelihood function can be built after all the maintenance data can be divided into the following three categories: incomplete repair time data, complete repair time data, super-complete repair time data. According to the actual situation of the maintenance data, an appropriate approximation can be made and the approximate analytical solution can be obtained. The values of μ and σ can be obtained as the corresponding estimated value of unknown parameters. By effectively mining the latent sample information, the maintainability growth evaluation is logical and reasonable.
The evaluation of maintainability growth plays an important role in improving materiel system effectiveness. Based on field maintenance information, a maximum likelihood model is put forward to evaluate the maintainability growth at the system level and sub-system level. A maximum likelihood function can be built after all the maintenance data can be divided into the following three categories: incomplete repair time data, complete repair time data, super-complete repair time data, according to the actual situation of the maintenance data, an appropriate approximation can be made and the approximate The value of μ and σ can be obtained as the corresponding estimated value of unknown parameters. By effectively mining the latent sample information, the maintainability growth evaluation is logical and reasonable.