To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies.
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Research-Article
Active Resource Allocation for Reliability Analysis With Model Bias Correction
Mingyang Li,
Mingyang Li
Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: mli7@mtu.edu
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: mli7@mtu.edu
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Zequn Wang
Zequn Wang
Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: zequnw@mtu.edu
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: zequnw@mtu.edu
Search for other works by this author on:
Mingyang Li
Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: mli7@mtu.edu
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: mli7@mtu.edu
Zequn Wang
Department of Mechanical Engineering-
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: zequnw@mtu.edu
Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49931
e-mail: zequnw@mtu.edu
1Corresponding author.
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 13, 2018; final manuscript received December 16, 2018; published online January 11, 2019. Assoc. Editor: Gary Wang.
J. Mech. Des. May 2019, 141(5): 051403 (13 pages)
Published Online: January 11, 2019
Article history
Received:
July 13, 2018
Revised:
December 16, 2018
Citation
Li, M., and Wang, Z. (January 11, 2019). "Active Resource Allocation for Reliability Analysis With Model Bias Correction." ASME. J. Mech. Des. May 2019; 141(5): 051403. https://doi.org/10.1115/1.4042344
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