A single-loop Kriging surrogate model method by considering the first failure instant for time-dependent reliability analysis and safety lifetime analysis. (November 2020)

• Record Type: Journal Article
• Authors: Hu, Yingshi; Lu, Zhenzhou; Wei, Ning; Zhou, Changcong
• Is Part Of: Mechanical systems and signal processing. Volume 145(2020)
• Publication Date: 2020-11
• Subjects: Time-dependent reliability analysis -- First failure instant -- Kriging surrogate model -- Safety lifetime analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106963
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Propose a new learning function of the Kriging method for the safety lifetime analysis. Considering the first failure instant in the Kriging model improves the computational efficiency. Illustrate the high efficiency and accuracy by comparing with other single-loop Kriging surrogate model methods. Abstract: The safety lifetime analysis under the constraint of a required time-dependent failure probability is of great significance to ensure the structural safety, and the vital point in safety lifetime analysis is efficiently estimating the time-dependent failure probability in any subinterval of the time interval of interest. For addressing this issue, a new single-loop Kriging method is proposed by considering the first failure instant, and a new learning function is constructed for selecting the most contributing candidate point to accurately recognize the first failure instant. Then the Kriging model can converge to accurately recognize the first failure instant. By this convergent Kriging model, the time-dependent failure probability in any subinterval of the time interval of interest can be estimated accurately, and then the safety lifetime can be obtained by dichotomy search directly. According to the strategy of identifying the first failure instant of each input sample, the proposed method is more efficient than the existing method named the single-loop adaptive sampling Kriging surrogate model. Three examples are used to validate the accuracy and efficiency of the proposed method.