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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
zhmp7@mst.edu

Xiaoping Du

Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology
dux@mst.edu

1Corresponding author.

ASME doi:10.1115/1.4039770 History: Received September 20, 2017; Revised March 21, 2018

Abstract

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 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 to infer a linear limit-sate function that reveals the relationship between the 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.

Copyright (c) 2018 by ASME
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