A novel imprecise stochastic process model for time-variant or dynamic uncertainty quantification

This paper proposes a novel model named as imprecise stochastic process model to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise prob-ability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the cross -correlation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliabil-ity analysis approach is developed based on the P-box-based imprecise stochastic process model, through which the interval of dynamic reliability for a structure under uncertain dynamic excita-tions or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples. (c) 2022 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes a novel model called the imprecise stochastic process model for handling the dynamic uncertainty in real-world problems. It introduces the definition and categorization of the P-box-based imprecise stochastic process and presents a time-variant reliability analysis approach. The effectiveness of the proposed method is verified through numerical examples.