DocumentCode
2929943
Title
"DGM-AMSAA" Model of reliability growth based on the small sample
Author
Xingzi Zhu ; Zhigeng Fang
Author_Institution
Sch. of Econ. & Manage., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
fYear
2013
fDate
15-17 Nov. 2013
Firstpage
123
Lastpage
126
Abstract
It is of great significance to the development of the reliability assessment of small-sample weapon equipment. According to the United States Army Materiel Systems Analysis Center (AMSAA) growth model, in this paper, we offer the DGM-AMSAA model of reliability growth for the poor information characteristics of weapon equipment. Firstly, we determine the reliability of the system is increased according to the Laplace test. Then, using the grey DGM (1,1) model to forecast to get the parameter estimation, which is based on DGM-AMSAA model. Finally, based on the Bayes theory, we use the prediction information obtained from the test data of N products as the prior information of a n+1 batch products, therefore, obtains the prior distribution. Combined with a small amount of field test data, we analyses the reliability of the system. A numerical example is given to verify the effectiveness of this method, and provides a new method for system reliability growth evaluation for small sample and poor information.
Keywords
Bayes methods; forecasting theory; grey systems; numerical analysis; parameter estimation; reliability theory; weapons; Bayes theory; DGM-AMSAA reliability growth model; Laplace test; United States Army Materiel Systems Analysis Center; field test data; grey DGM (1,1) model; numerical analysis; parameter estimation; prediction information; small-sample weapon equipment reliability assessment; system reliability growth evaluation; weapon equipment information characteristics; Analytical models; Data models; Estimation; Parameter estimation; Predictive models; Reliability theory; 1) model; AMSAA model; Bayes theory; DGM(1; reliability; small sample;
fLanguage
English
Publisher
ieee
Conference_Titel
Grey Systems and Intelligent Services, 2013 IEEE International Conference on
Conference_Location
Macao
ISSN
2166-9430
Print_ISBN
978-1-4673-5247-5
Type
conf
DOI
10.1109/GSIS.2013.6714746
Filename
6714746
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