Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

Reliability Analysis of Mechanical Products Based on Regenerative Samples

Authors
Tengfei Chen, Erling Gong
Corresponding Author
Tengfei Chen
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.126How to use a DOI?
Keywords
Mechanical products; small sample; time censoring; Bootstrap; BP neural network
Abstract
Aiming at the problem of reliability estimation of mechanical products in small sample size and time censored test under Weibull distribution, this paper proposed a method which combined Bootstrap and BP neural network for reliability evaluation. Firstly, Bootstrap method was used to expand the reliability and failure time sample. Secondly, the BP neural network was trained by the expanded sample. Then the parameters of Weibull distribution can be estimated by the trained BP neural network. Finally, the reliability characteristics of the product can be obtained. In the end, an example was analyzed to illustrate the applicability of the method.
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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/ammee-17.2017.126How 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  - Tengfei Chen
AU  - Erling Gong
PY  - 2017/06
DA  - 2017/06
TI  - Reliability Analysis of Mechanical Products Based on Regenerative Samples
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.126
DO  - https://doi.org/10.2991/ammee-17.2017.126
ID  - Chen2017/06
ER  -