Reliability Growth Prediction Based on an Improved Grey Prediction Model
Author(s) -
Yuhong Wang,
Yaoguo Dang,
Sifeng Liu
Publication year - 2010
Publication title -
international journal of computational intelligence systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.385
H-Index - 41
eISSN - 1875-6891
pISSN - 1875-6883
DOI - 10.1080/18756891.2010.9727697
Subject(s) - reliability (semiconductor) , computer science , sequence (biology) , reliability engineering , set (abstract data type) , raw data , function (biology) , sample (material) , growth curve (statistics) , growth model , data mining , statistics , mathematics , engineering , power (physics) , chemistry , physics , chromatography , quantum mechanics , evolutionary biology , biology , genetics , programming language , mathematical economics
As limits of time, labors and expenses, observed data usually have the characteristic of small sample sizes in development test program. Redesigns or corrective actions can result in changes of reliability for equipments. We propose an improved GM(1,1) model to predict reliability growth in this paper. First, a newly initial condition in time response function is set in this improved GM(1,1) model. The newly initial condition is comprised of the first item and the last item of a sequence which is generated from applying the first-order accumulated generation operator to a sequence of raw data. Then the improved model can express the principle of new information priority well and improve prediction precision through fully applying new information in raw data. Secondly, we make use of the improved model to predict reliability growth in a numerical example. The comparison of predicted reliability growth curve from the improved GM(1,1) model and that from the Lloyd–Lipow model indicates that the improved GM(1...
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