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| Structural uncertainty modeling and propagation based on principal component analysis |
| Received:April 18, 2016 Revised:October 16, 2016 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx201704002 |
| KeyWord:uncertainty modeling principal component analysis non-probabilistic convex model uncertainty propagation interval model correlation |
| Author | Institution |
| 刘杰 |
湖南大学 机械与运载工程学院 汽车车身先进设计制造国家重点实验室, 长沙 |
| 谢凌 |
湖南大学 机械与运载工程学院 汽车车身先进设计制造国家重点实验室, 长沙 |
| 卿宏军 |
湖南大学 机械与运载工程学院 汽车车身先进设计制造国家重点实验室, 长沙 |
| 刘浩 |
湖南大学 机械与运载工程学院 汽车车身先进设计制造国家重点实验室, 长沙 |
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| Abstract: |
| This paper proposes a new structural uncertainty modeling method based on principal component analysis.First,the sample data of uncertain structure parameters are analyzed through principal component analysis method,and the corresponding orthogonal eigenvectors can be obtained.Then the sample data are projected to the new coordinate system which are established based on the eigenvector direction.Finally,the boundaries of uncertain parameters on the new coordinate system are calculated so that the non-probabilistic interval model for modeling the uncertainties of structure parameters is established.The uncertainty model based on principal component analysis is relatively compact,and it can transform the correlated parameters to uncorrelated parameters while the uncertainty model is established,which is convenient to efficiently solve uncertainty propagation problems.Two examples of uncertainty propagation that compared with the traditional interval model and parallelepiped model demonstrate the correctness and effectiveness of the proposed method. |