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Research Papers: Design Automation

A Novel Time-Variant Reliability Analysis Method Based on Failure Processes Decomposition for Dynamic Uncertain Structures

[+] Author and Article Information
Shui Yu

School of Mechanical and Electrical Engineering,
University of Electronic Science
and Technology of China,
Chengdu 611731, China
e-mail: yushuiuestc@163.com

Zhonglai Wang

School of Mechanical and Electrical Engineering,
University of Electronic Science
and Technology of China,
Chengdu 611731, China;
Center for System Reliability and Safety,
University of Electronic Science
and Technology of China,
Chengdu 611731, China
e-mail: wzhonglai@uestc.edu.cn

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 25, 2017; final manuscript received February 8, 2018; published online March 9, 2018. Assoc. Editor: Xiaoping Du.

J. Mech. Des 140(5), 051401 (Mar 09, 2018) (11 pages) Paper No: MD-17-1652; doi: 10.1115/1.4039387 History: Received September 25, 2017; Revised February 08, 2018

Due to the uncertainties and the dynamic parameters from design, manufacturing, and working conditions, many engineering structures usually show uncertain and dynamic properties. This paper proposes a novel time-variant reliability analysis method using failure processes decomposition to transform the time-variant reliability problems to the time-invariant problems for dynamic structures under uncertainties. The transformation is achieved via a two-stage failure processes decomposition. First, the limit state function with high dimensional input variables and high order temporal parameters is transformed to a quadratic function of time based on the optimized time point in the first-stage failure processes decomposition. Second, based on the characteristics of the quadratic function and reliability criterion, the time-variant reliability problem is then transformed to a time-invariant system reliability problem in the second-stage failure processes decomposition. Then, the kernel density estimation (KDE) method is finally employed for the system reliability evaluation. Several examples are used to verify the effectiveness of the proposed method to demonstrate its efficiency and accuracy.

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Figures

Grahic Jump Location
Fig. 1

The geometrical relationship between gT(d,X,Y(N,T),T) and T

Grahic Jump Location
Fig. 2

Flowchart of the proposed method

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Fig. 3

A corroded simple supported beam under random loadings

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Fig. 4

A ten-bar truss structure

Grahic Jump Location
Fig. 6

Configuration for the hip joint

Grahic Jump Location
Fig. 8

Reliability for the knee joint

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Fig. 9

Reliability for the ankle joint

Grahic Jump Location
Fig. 10

Reliability for the LEEX

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Fig. 5

The simplified configuration of a powered anthropomorphic leg for the LEEX

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Fig. 7

Reliability for the hip joint

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