Combined sensitivity analysis for multiple failure modes

Jiannan Yang; Arnau Clot; Robin S. Langley

doi:10.1016/j.cma.2022.115030

Combined sensitivity analysis for multiple failure modes

Jiannan Yang^*, Arnau Clot, Robin S. Langley

^*Corresponding author for this work

Physics

Research output: Contribution to journal › Article › peer-review

Abstract

Sensitivity analysis identifies the influential parameters and it is a key step in the design process. Focusing on reliability based design, the probability of a failure mode and its sensitivity to input uncertainties are obtained in a single Monte Carlo simulation using the Likelihood Ratio method. For correlated multiple failure modes, a sensitivity matrix is proposed. The dominant singular vectors of the sensitivity matrix are found to indicate the most important parameters for combined sensitivity of multiple failure modes. Therefore, the sensitivity matrix can guide resources allocation so that most uncertainties can be reduced for system reliabilities.

Original language	English
Article number	115030
Number of pages	17
Journal	Computer Methods in Applied Mechanics and Engineering
Volume	395
Early online date	10 May 2022
DOIs	https://doi.org/10.1016/j.cma.2022.115030
Publication status	Published - 15 May 2022

Bibliographical note

Funding Information:
This work has been funded by the Engineering and Physical Sciences Research Council, UK through the award of a Programme Grant “Digital Twins for Improved Dynamic Design”, Grant No. EP/R006768 .

Publisher Copyright:
© 2022 The Author(s)

Keywords

Correlated failure modes
Likelihood ratio method
Marine riser
Sensitivity matrix
Sensitivity to distribution parameters
System reliability

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Combined sensitivity analysis for multiple failure modes
© 2022 The Author(s).
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Cite this

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abstract = "Sensitivity analysis identifies the influential parameters and it is a key step in the design process. Focusing on reliability based design, the probability of a failure mode and its sensitivity to input uncertainties are obtained in a single Monte Carlo simulation using the Likelihood Ratio method. For correlated multiple failure modes, a sensitivity matrix is proposed. The dominant singular vectors of the sensitivity matrix are found to indicate the most important parameters for combined sensitivity of multiple failure modes. Therefore, the sensitivity matrix can guide resources allocation so that most uncertainties can be reduced for system reliabilities.",

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AU - Langley, Robin S.

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PY - 2022/5/15

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N2 - Sensitivity analysis identifies the influential parameters and it is a key step in the design process. Focusing on reliability based design, the probability of a failure mode and its sensitivity to input uncertainties are obtained in a single Monte Carlo simulation using the Likelihood Ratio method. For correlated multiple failure modes, a sensitivity matrix is proposed. The dominant singular vectors of the sensitivity matrix are found to indicate the most important parameters for combined sensitivity of multiple failure modes. Therefore, the sensitivity matrix can guide resources allocation so that most uncertainties can be reduced for system reliabilities.

AB - Sensitivity analysis identifies the influential parameters and it is a key step in the design process. Focusing on reliability based design, the probability of a failure mode and its sensitivity to input uncertainties are obtained in a single Monte Carlo simulation using the Likelihood Ratio method. For correlated multiple failure modes, a sensitivity matrix is proposed. The dominant singular vectors of the sensitivity matrix are found to indicate the most important parameters for combined sensitivity of multiple failure modes. Therefore, the sensitivity matrix can guide resources allocation so that most uncertainties can be reduced for system reliabilities.

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KW - Likelihood ratio method

KW - Marine riser

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KW - Sensitivity to distribution parameters

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Combined sensitivity analysis for multiple failure modes

Abstract

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