Maintainability Data Decision Methodology (MDDM)
Abstract
Organizations within the U.S. Army [i.e. Communications-Electronics Command (CECOM)] and other government organizations have the need to evaluate Maintenance Manpower requirements for systems (i.e. Power Generators, etc.) where fully developed maintenance data is NOT available. Maintenance Manpower requirements are computed by multiplying an estimated maintenance ratio (man-hours per usage) by a one year wartime usage, which results in a total number of recommended maintenance man-hours. Army organizations need to know how much maintenance ratio data needs to be collected until the sample can be used to generate maintenance manpower requirements. AMSAA developed a maintainability data decisioning methodology (MDDM) which determines if enough sample maintenance data exists in order to infer the true fleet maintenance ratio (MR). This will allow the Army to make manpower requirement determinations. MDDM uses a systems aging model, parametric & nonparametric empirical bayes models, two stochastic inferencing and four coverage validation models using nonparametric & parametric bootstrapping, percentile method with bias correction & acceleration using jackknifing, Monte Carlo simulation, and other stochastic modeling techniques & processes. AMSAA has applied MDDM to CECOM Power Generators. Accuracy and precision of the inference is what determines if enough data exists. A coverage validation model is used to measure accuracy and precision is measured by the size of the inference. MDDM is a decisioning process and can be applied to other Army weapon systems where the need exists.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2011
- Accession Number
- ADA548943
Entities
People
- John Nierwinski Jr.
Organizations
- United States Army Materiel Systems Analysis Activity