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Technical Brief

Integration of Statistics- and Physics-Based Methods—A Feasibility Study on Accurate System Reliability Prediction

[+] Author and Article Information
Zhengwei Hu

Department of Mechanical and Aerospace Engineering,
Missouri University of Science and Technology,
258A Toomey Hall, 400 West 13th Street,
Rolla, MO 65409-0500
e-mail: zhmp7@mst.edu

Xiaoping Du

Department of Mechanical
and Aerospace Engineering,
Missouri University of Science and Technology,
272 Toomey Hall, 400 West 13th Street,
Rolla, MO 65409-0500
e-mail: dux@mst.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 20, 2017; final manuscript received March 21, 2018; published online May 11, 2018. Assoc. Editor: Samy Missoum.

J. Mech. Des 140(7), 074501 (May 11, 2018) (7 pages) Paper No: MD-17-1638; doi: 10.1115/1.4039770 History: Received September 20, 2017; Revised March 21, 2018

Component reliability can be estimated by either statistics-based methods with data or physics-based methods with models. Both types of methods are usually independently applied, making it difficult to estimate the joint probability density of component states, which is a necessity for an accurate system reliability prediction. The objective of this study is to investigate the feasibility of integrating statistics- and physics-based methods for system reliability analysis. The proposed method employs the first-order reliability method (FORM) directly for a component whose reliability is estimated by a physics-based method. For a component whose reliability is estimated by a statistics-based method, the proposed method applies a supervised learning strategy through support vector machines (SVM) to infer a linear limit-state function that reveals the relationship between component states and basic random variables. With the integration of statistics- and physics-based methods, the limit-state functions of all the components in the system will then be available. As a result, it is possible to predict the system reliability accurately with all the limit-state functions obtained from both statistics- and physics-based reliability methods.

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References

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Figures

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Fig. 1

Marginal classifiers along with SVs

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Fig. 2

Classification of training points using SVM

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Fig. 3

A cantilever beam system

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Fig. 4

A crank-slider system

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