Research Papers: Design Automation

A Radial-Based Centralized Kriging Method for System Reliability Assessment

[+] Author and Article Information
Yao Wang

China Academy of Launch Vehicle Technology,
Beijing 100076, China
e-mail: wangyao87@hotmail.com

Dongpao Hong

China Academy of Launch Vehicle Technology,
Beijing 100076, China
e-mail: dp_hong@hotmail.com

Xiaodong Ma

China Academy of Launch Vehicle Technology,
Beijing 100076, China
e-mail: ma_xiaodong@hotmail.com

Hairui Zhang

China Academy of Launch Vehicle Technology,
Beijing 100076, China
e-mail: zhang_hairui@hotmail.com

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 16, 2017; final manuscript received April 1, 2018; published online May 11, 2018. Assoc. Editor: Xiaoping Du.

J. Mech. Des 140(7), 071403 (May 11, 2018) (11 pages) Paper No: MD-17-1694; doi: 10.1115/1.4039919 History: Received October 16, 2017; Revised April 01, 2018

System reliability assessment is a challenging task when using computationally intensive models. In this work, a radial-based centralized Kriging method (RCKM) is proposed for achieving high efficiency and accuracy. The method contains two components: Kriging-based system most probable point (MPP) search and radial-based centralized sampling. The former searches for the system MPP by progressively updating Kriging models regardless of the nonlinearity of the performance functions. The latter refines the Kriging models with the training points (TPs) collected from pregenerated samples. It concentrates the sampling in the important high-probability density region. Both components utilize a composite criterion to identify the critical Kriging models for system failure. The final Kriging models are sufficiently accurate only at those sections of the limit states that bound the system failure region. Its efficiency and accuracy are demonstrated via application to three examples.

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Fig. 1

Iterations of the Kriging-based MPP searching for system reliability

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Fig. 2

Illustration of the radial-based centralized sampling approach

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Fig. 3

Limit state contours and multiple MPPs of example 1: (a) c = 3, (b) c = 4, (c) c = 5

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Fig. 4

Resulting Kriging models and TPs for case 1 (c = 3). The solid lines represent the Kriging models that are the approximations of the dashed limit state contours for the two performance functions.

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Fig. 5

Resulting Kriging models and TPs for case 2 (c = 4). The solid lines represent the Kriging models that are the approximations of the dashed limit state contours for the two performance functions.

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Fig. 6

Resulting Kriging models and TPs for case 3 (c = 5). The solid lines represent the Kriging models that are the approximations of the dashed limit state contours for the two performance functions.




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