International Journal of Computational Intelligence Systems

Volume 3, Issue 3, September 2010, Pages 266 - 273

Reliability Growth Predication based on an Improved Grey Predicton Model

Authors
Yuhong Wang, Yaoguo Dang, Sifeng Liu
Corresponding Author
Yuhong Wang
Available Online 29 June 2010.
DOI
https://doi.org/10.2991/ijcis.2010.3.3.2How to use a DOI?
Keywords
reliability growth, GM(1,1) model, the initial condition, first-order accumulated generation operator.
Abstract
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(1,1) model is much better than the Lloyd-Lipow model for the reliability growth prediction.
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
3 - 3
Pages
266 - 273
Publication Date
2010/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2010.3.3.2How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Yuhong Wang
AU  - Yaoguo Dang
AU  - Sifeng Liu
PY  - 2010
DA  - 2010/06
TI  - Reliability Growth Predication based on an Improved Grey Predicton Model
JO  - International Journal of Computational Intelligence Systems
SP  - 266
EP  - 273
VL  - 3
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.3.2
DO  - https://doi.org/10.2991/ijcis.2010.3.3.2
ID  - Wang2010
ER  -