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.
Active Resource Allocation for Reliability Analysis With Model Bias Correction
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.
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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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