Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)

Research on Reliability Assessment Based on the Multi-Source Information Fusion Technology with Improved Inheritance Factor

Authors
Jing-Bo Gai, Gao-Feng Wei
Corresponding Author
Jing-Bo Gai
Available Online December 2016.
DOI
https://doi.org/10.2991/mme-16.2017.5How to use a DOI?
Keywords
Reliability assessment, Information fusion technology, Inheritance factor, Complex system with small samples.
Abstract
In order to solve the problems of the classical system reliability assessment method in dealing with complex system with small samples, such as multilevel approximation, insufficiency and singleness of information, take the success or failure products as an example, the theory of multi-source reliability information fusion based on inheritance factor is introduced to solve the problem of reliability assessment of complex system with small samples. The method of determining the inheritance factor is improved, and a method to determine the inheritance factor by the goodness of fit is given. Taking a small samples reliability assessment of a certain type of solid rocket motor as an example, the rationality of the present method and its application prospect are indicated.
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Proceedings
3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
Part of series
Advances in Engineering Research
Publication Date
December 2016
ISBN
978-94-6252-303-6
ISSN
2352-5401
DOI
https://doi.org/10.2991/mme-16.2017.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jing-Bo Gai
AU  - Gao-Feng Wei
PY  - 2016/12
DA  - 2016/12
TI  - Research on Reliability Assessment Based on the Multi-Source Information Fusion Technology with Improved Inheritance Factor
BT  - 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mme-16.2017.5
DO  - https://doi.org/10.2991/mme-16.2017.5
ID  - Gai2016/12
ER  -