Fuzzy Synthetic Evaluation Approach of Reliability for Condition Monitoring System

Hu, Youmin; Wu, Bo; Yi, Pongxin

doi:10.1007/978-3-540-88518-4_63

Cited by 1 publication

(3 citation statements)

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“…The influence factor set, comment set, single factor assessment (or called fuzzy relation matrix) and evaluation set are the four key elements in FSA method (Zhao et al 2004a,b;Hu et al 2008). The influence factor set is a finite discrete crisp set containing various possible factors that affect the product reliability, and it is denoted by:…”

Section: Fsa Methodsmentioning

confidence: 99%

“…(4) Fuzzy synthetic assessment (FSA) method which incorporates the experts' experiences and judgements via fuzzy set theory (Zhao, Wen, and Duan 2004a;Hu et al 2008). Other reliability prediction methods have also been proposed in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…As a matter of fact, many factors influence the product reliability. Due to the lack of sufficient test data, as well as the complicated interactive impacts among factors, it is often difficult, even impossible, to assess the exact weights and impacts imposed by these factors (Hu, Wu, and Yi 2008). On the other hand, uncertainties resulting from the lack of sufficient knowledge of underlying failure physics and subjective judgements from domain experts widely exist at the conceptual design stage.…”

Section: Introductionmentioning

confidence: 99%

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Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Liu

Huang

Liu

2013

International Journal of Systems Science

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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.

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Section: Fsa Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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