System reliability analysis through active learning Kriging model with truncated candidate region

- Existing strategies fail to identify the insignificant component(s) if large numerical difference exists.
- A brand-new theory to circumvent the shortcoming is proposed.
- A method based on ALK model with a truncated candidate region is proposed.
- The performance of the proposed method is compared with the existing methods.

System reliability analysis (SRA) with multiple failure modes is researched in this paper. Active learning Kriging (ALK) model which only finely approximates the performance function in the narrow region close to the limit state has shown great potential and several strategies based on ALK model have been proposed. The key of SRA based on ALK model is to identify the components with little contribution to system failure and avoid approximating them. However, we figure out that the existing strategies fail to fulfill this task if large numerical difference exists among the values of component performance functions. Therefore, a brand-new theory on identifying the unimportant component(s) is proposed. Based on this theory, the method based on ALK model with a truncated candidate region (TCR) is proposed and it is termed as ALK-TCR. ALK-TCR is capable to recognize and avoid approximating the unimportant component(s), even if large numerical difference arises among the components. Its high performance is demonstrated by three complicated examples.