期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 198, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2020.106906
关键词
System reliability analysis; Real-time estimation error; Wrong state classification; Adaptive Kriging; Multiple failure modes
资金
- National Natural Science Foundation of China [51675198]
- National Defense Pre-Research Foundation of China [41423010205]
- National Science Foundation for Distinguished Young Scholars of China [51825502]
- Program for HUST Academic Frontier Youth Team [2017QYTD04]
In order to approximate the multiple limit state functions for different failure events, the active learning Kriging model proposed for component reliability analysis has been extended to system reliability analysis. Meanwhile, many efficient sampling strategies have been applied to reduce the high computational burden. However, these strategies meet a challenge in wasting some training points and terminating the training process inappropriately, since they do not directly relate to the estimation error of system failure probability. To address the challenge, this work proposes an estimation error-guided adaptive Kriging method. As Kriging prediction may be inaccurate before being well trained, the predicted system failure probability may deviate from the true result. To quantify this estimation error, the true number of failure points is approximated by adding the number of predicted failure points and the number of wrongly classified points. Since it is impossible to learn the exact number of wrongly classified points, its confidence interval is derived based on the probability of making wrong state classification. Subsequently, the refinement of Kriging is achieved by using the probability to identify new points and using the estimation error to determine the termination, which has been demonstrated by three different cases.
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