Applied Mathematics and Mechanics (English Edition) ›› 2005, Vol. 26 ›› Issue (9): 1121-1125 .

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RESEARCH ON RELIABILITY GROWTH FOR SYNCHRONOUSLY DEVELOPED MULTI-SYSTEMS

马小宁, 吕震宙, 岳珠峰   

  • 收稿日期:2003-10-14 修回日期:2005-04-26 出版日期:2005-09-18 发布日期:2005-09-18
  • 通讯作者: 吕震宙

RESEARCH ON RELIABILITY GROWTH FOR SYNCHRONOUSLY DEVELOPED MULTI-SYSTEMS

MA Xiao-ning, LÜ Zhen-zhou, YUE Zhu-feng   

  1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, P.R.China
  • Received:2003-10-14 Revised:2005-04-26 Online:2005-09-18 Published:2005-09-18
  • Contact: LÜ Zhen-zhou

Abstract: An advanced reliability growth model, i.e. exponential model, was presented to estimate the model parameters for multi-systems, which was synchronously tested, synchronously censored, and synchronously improved. In the presented method, the data during the reliability growth process were taken into consideration sufficiently, including the failure numbers, safety numbers and failure time at each censored time. If the multi-systems were synchronously improved for many times, and the reliability growth of each system fitted AMSAA (Army Material Systems Analysis Activity) model, the failure time of each system could be considered rationally as an exponential distribution between two adjoining censored times. The nonparametric method was employed to obtain the reliability at each censored time of the synchronous multi-systems. The point estimations of the model parameters, a and b, were given by the least square method. The confidence interval for the parameter b was given as well. An engineering illustration was used to compare the result of the presented method with those of the available models. The result shows that the presented exponential growth model fits AMSAA-BISE (Army Material Systems Analysis Activity-Beijing Institute of Structure and Environment) model rather well, and two models are suitable to estimate the reliability growth for the synchronously developed multi-systems.

Key words: reliability growth, least square method, AMSAA model, AMSAA-BISE model, non-homogeneity Poisson process

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