Name
Title | ||
|---|---|---|
Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment |
Abstract | ||
|---|---|---|
Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices such as reliability, availability and mean time to failure before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment FSA approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1080/00207721.2011.617887 | Int. J. Systems Science |
Keywords | Field | DocType |
reliability index,reliability prediction,accurate prediction,new evolutionary product,various reliability,similar product,evolutionary product,network-based fuzzy synthetic assessment,conceptual design phase,domain expert,conceptual design,comparative study,mean time to failure,fuzzy set,product lifecycle management,neural network | Mean time between failures,Conceptual design,Data mining,Data set,Fuzzy logic,Fuzzy set,Artificial neural network,Product lifecycle,Mathematics,New product development | Journal |
Volume | Issue | ISSN |
44 | 3 | 0020-7721 |
Citations | PageRank | References |
6 | 0.56 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 190 | 19.09 |
| Hong-Zhong Huang | 2 | 583 | 58.24 |
| Dan Ling | 3 | 8 | 1.29 |