An efficient D-vine copula-based coupling uncertainty analysis for variable-stiffness composites

Li, Qidi; Cai, Yong; Wang, Hu; Lv, Zhiwei; Li, Enying

doi:10.1016/j.compstruct.2019.03.067

Cited by 5 publications

(1 citation statement)

References 53 publications

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“…The vine copula structure has a diverse decomposition form; thus, the appropriate vine structure needs to be determined before modeling. Commonly used structures include the C-vine and D-vine. , The C-vine is a star structure characterized by a dominant node (dominant variable) in each tree; the relationship between the dominant variable and other variables need to be established. Meanwhile, the D-vine is a linear structure; no dominant variable exists in each tree.…”

Section: Introductionmentioning

confidence: 99%

Soft-Sensor Model for Chemical Processes Based on D-Vine Copula with Rolling Pin Transformation

Cai

Zhou

Zhang

et al. 2019

Ind. Eng. Chem. Res.

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Soft-sensing methods have been widely used in recent years to predict key variables that are difficult to measure or involve costly and time-consuming in chemical processes. Owing to the increasing complexity of industrial processes, industrial data often exhibit strong nonlinearities and self-correlation, and the data distribution fails to satisfy the Gaussian assumption. To address these problems, a vine copula-based soft-sensor model combined with the rolling pin method is proposed. This approach uses a D-vine copula to model a joint probability distribution of auxiliary variables and a key variable. In accordance with the joint distribution, the predicted value of the key variable can be determined by weighting the training samples. During modeling, the Bayesian information criterion is adopted to select the best-fitted copula pairs. Given the nonmonotonicity between variables of actual industrial data, the rolling pin monotonic transformation is simultaneously introduced to improve the suitability of transformed data for vine copula modeling. The efficiency of the proposed method was verified using a numerical example and ethylene cracking.

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Section: Introductionmentioning

confidence: 99%