A single-loop strategy for time-variant system reliability analysis under multiple failure modes. (1st February 2021)

• Record Type: Journal Article
• Authors: Qian, Hua-Ming; Huang, Tudi; Huang, Hong-Zhong
• Is Part Of: Mechanical systems and signal processing. Volume 148(2021)
• Publication Date: 2021-02-01
• Subjects: Time-variant system -- Reliability analysis -- MRGP -- Kriging -- Single loop -- Learning function; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107159
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: This paper proposes a novel single-loop strategy for time-variant system reliability analysis by combining multiple response Gaussian process and Kriging model. The flowchart of the proposed method is presented in following figure. Highlights: A new single-loop strategy is proposed to decouple the double-loop procedure for time-variant reliability analysis. The multiple response Gaussian process and Kriging model are combined for time-variant reliability analysis. Different searching strategies are presented to choose the new point and the effectiveness is also testified. Abstract: This paper proposes a single-loop strategy for time-variant system reliability analysis by combining multiple response Gaussian process (MRGP) and Kriging model. Firstly, a new strategy is provided to decouple the double-loop strategy of extreme-value-based time-variant system reliability analysis, where the extreme value response in the double-loop strategy is approximated by the best value in the initial points to avoid the inner-loop extremal optimization. Then, the extreme value response surface is directly built based on the approximated extreme value using the MRGP model. Meanwhile, a Kriging model based on initial points is also proposed to search a new sample point. Furthermore, in order to update the initial sample points and improve the accuracy of the proposed strategy, three learning functions (U-function, EFF-function and H-function) and expected improvement (EI) function are combined to select a new point that resides as close to the extreme value response surface as possible. Finally, several examples are presented to demonstrate the effectiveness of the proposed strategy.