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Multivariate global sensitivity analysis for dynamic models based on wavelet analysis

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  • Xiao, Sinan
  • Lu, Zhenzhou
  • Wang, Pan

Abstract

Dynamic models with time-dependent output are widely used in engineering for risk assessment and decision making. Global sensitivity analysis for these models is very useful for simplifying the model, improving the model performance, etc. The existent covariance decomposition based global sensitivity analysis method combines the variance based sensitivity analysis results of the model output at all the instants, which just utilizes the information of the time-dependent output in time domain. However, many significant features of time-dependent output may not be obtained from the time domain. Thus, performing global sensitivity analysis for dynamic models just with the information in time domain may be incomplete. In this paper, a new kind of sensitivity indices based on wavelet analysis is proposed. The energy distribution of model output over different frequency bands is extracted as a quantitative feature of the time-dependent output, and it contains the information of model output in both time and frequency domains. Then, a vector projection method is utilized to measure the effects of input variables on the energy distribution of model output. An efficient algorithm is also proposed to estimate the new sensitivity indices. The numerical examples show the difference between the new sensitivity indices and the covariance decomposition based sensitivity indices. Finally, the new sensitivity indices are applied to an environmental model to tell the relative importance of the input variables, which can be useful for improving the model performance.

Suggested Citation

  • Xiao, Sinan & Lu, Zhenzhou & Wang, Pan, 2018. "Multivariate global sensitivity analysis for dynamic models based on wavelet analysis," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 20-30.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:20-30
    DOI: 10.1016/j.ress.2017.10.007
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    References listed on IDEAS

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    Cited by:

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    2. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
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    4. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    6. Xiao, Sinan & Praditia, Timothy & Oladyshkin, Sergey & Nowak, Wolfgang, 2021. "Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis," Applied Energy, Elsevier, vol. 285(C).
    7. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    8. Daniel W. Gladish & Ross Darnell & Peter J. Thorburn & Bhakti Haldankar, 2019. "Emulated Multivariate Global Sensitivity Analysis for Complex Computer Models Applied to Agricultural Simulators," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 130-153, March.
    9. Chen, Xin & Molina-Cristóbal, Arturo & Guenov, Marin D. & Riaz, Atif, 2019. "Efficient method for variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 97-115.
    10. Yicheng Zhou & Zhenzhou Lu & Yan Shi & Kai Cheng, 2019. "A vine copula–based method for analyzing the moment-independent importance measure of the multivariate output," Journal of Risk and Reliability, , vol. 233(3), pages 338-354, June.
    11. Zhaoxia Xu & Xiaoping Zhou & Qihu Qian, 2021. "The global sensitivity analysis of slope stability based on the least angle regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2361-2379, February.
    12. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

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