RESEARCH PAPER
An adaptive Kriging method focusing on reliability-sensitive space-time for time-variant reliability analysis
1
School of Reliability and Systems Engineering, Beihang University, China
2
Hangzhou International Innovation Institute, Beihang University, China
3
School of Computer Science and Engineering, Beihang University, China
4
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, China
Submission date: 2024-12-28
Final revision date: 2025-03-10
Acceptance date: 2025-04-11
Online publication date: 2025-04-17
Publication date: 2025-04-17
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):203980
HIGHLIGHTS
- A new single loop Kriging method for time-variant reliability analysis is proposed.
- A reliability-sensitive region-based adaptive learning mechanism is designed.
- Four case studies illustrate the superiority of the proposed approach.
- This study clarifies the importance of focusing reliability-sensitive space-time region.
KEYWORDS
Time-variant reliability analysisAdaptive Kriging modelingSingle loopReliability-sensitive space-time region
TOPICS
ABSTRACT
The time-variant reliability analysis method based on the adaptive single-loop surrogate has attracted much attention due to its excellent computing performance. However, the existing methods do not sufficiently focus surrogate learning on the reliability-sensitive space-time region with high efficacy in improving the reliability surrogate, resulting in calculating waste. In this paper, a reliability-sensitive space-time Kriging (RSTK) modeling approach is proposed. In the RSTK, to screen out reliability-sensitive trajectory segments, a reliability-sensitive space-time determination method is first proposed; further, to capture high-quality training samples, a reliability-sensitive space-time learning approach is designed correspondingly; finally, a matching iteration termination criterion is constructed. Four case studies demonstrate the superiority of the proposed RSTK in reducing calculational costs. RSTK shortens the iteration time by one to two orders of magnitude and reduces the surrogate cost by up to 22.3% while maintaining accuracy.
ACKNOWLEDGEMENTS
This paper is co-supported by the National Natural Science Foundation of China (No. 52205164 and 52405145), the Research Start-up Funds of Hangzhou International Innovation Institute of Beihang University (No. 2024KQ085), and the Insight Action (No. F5C15B07). The authors would like to thank them.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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