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Research Papers

A Time-Variant Reliability Analysis Method Based on Stochastic Process Discretization

[+] Author and Article Information
C. Jiang

State Key Laboratory of Advanced Design
and Manufacturing for Vehicle Body,
College of Mechanical and Vehicle Engineering,
Hunan University,
Changsha 410082, China
e-mail: jiangc@hnu.edu.cn

X. P. Huang

State Key Laboratory of Advanced Design
and Manufacturing for Vehicle Body,
College of Mechanical and Vehicle Engineering,
Hunan University,
Changsha 410082, China
e-mail: huangxinping2501@163.com

X. Han

State Key Laboratory of Advanced Design
and Manufacturing for Vehicle Body,
College of Mechanical and Vehicle Engineering,
Hunan University,
Changsha 410082, China
e-mail: hanxu@hnu.edu.cn

D. Q. Zhang

State Key Laboratory of Advanced Design
and Manufacturing for Vehicle Body,
College of Mechanical and Vehicle Engineering,
Hunan University,
Changsha 410082, China
e-mail: zhangdq@hnu.edu.cn

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 24, 2013; final manuscript received May 12, 2014; published online July 2, 2014. Assoc. Editor: Xiaoping Du.

J. Mech. Des 136(9), 091009 (Jul 02, 2014) (11 pages) Paper No: MD-13-1375; doi: 10.1115/1.4027865 History: Received August 24, 2013; Revised May 12, 2014

Time-variant reliability problems caused by deterioration in material properties, dynamic load uncertainty, and other causes are widespread among practical engineering applications. This study proposes a novel time-variant reliability analysis method based on stochastic process discretization (TRPD), which provides an effective analytical tool for assessing design reliability over the whole lifecycle of a complex structure. Using time discretization, a stochastic process can be converted into random variables, thereby transforming a time-variant reliability problem into a conventional time-invariant system reliability problem. By linearizing the limit-state function with the first-order reliability method (FORM) and furthermore, introducing a new random variable, the converted system reliability problem can be efficiently solved. The TRPD avoids the calculation of outcrossing rates, which simplifies the process of solving time-variant reliability problems and produces high computational efficiency. Finally, three numerical examples are used to verify the effectiveness of this approach.

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Figures

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Fig. 1

A simple supported beam structure [37]

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Fig. 2

The curves indicating the reliability indices and failure probabilities for the simple supported beam over time

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Fig. 3

A cantilever tube [38]

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Fig. 4

The curves indicating the reliability indices and failure probabilities for the cantilever tube over time

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Fig. 5

A crankshaft model and its crank model for a particular type of engine

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Fig. 6

The pressure distributions in the connecting rod shaft [40]

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Fig. 7

The curves indicating the reliability indices and failure probabilities for the engine crankshaft over time

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