In order to effectively and accurately assess the failure probability of mechanical structures, this paper proposes a multi‐point sampling active learning reliability analysis method called AKMP. First, a GA‐Halton sequence is introduced to make the initial samples well dispersed and homogeneous in the design space. Second, a new learning function FELF is constructed to efficiently update the Kriging model, which takes into account the relationship between the location of the sampling points and the performance fun. Then, a combination of NCC criterion and multipoint sampling strategy is proposed to further improve the convergence efficiency, which can effectively terminate the active learning process. Finally, numerical and engineering cases are tested to verify the application performance of the proposed AKMP. The results show that the method has superior performance in terms of both accuracy and failure probability efficiency, and can reduce the computational resources of the active learning process.

The commonly used reliability analysis approaches for Kriging-based models are usually conducted based on high-fidelity Kriging models. However, high-fidelity surrogate models are commonly costly. Therefore, in order to balance the calculation expense and calculation time of the surrogate model, this paper proposes a multi-fidelity Kriging model reliability analysis approach with coupled optimal important sampling density (OISD+MFK). First, the MEI learning function is proposed considering the training sample distance, model computation cost, expected improvement function, and model relevance. Second, a dynamic stopping condition is proposed that takes into account the failure probability estimation error. Finally, the optimal importance sampling density is incorporated into the reliability analysis process, which can effectively reduce failure probability estimation error. The results of the study show that the approach proposed in this paper can reduce the calculation cost while outputting relatively accurate failure probability evaluation results.

In this study, material and dynamic stress experiments are combined with finite element (FE) simulations to reveal the fracture mechanism of the wheelset lifting apparatus, and a structural design optimization scheme based on the double-layer Kriging surrogate model is proposed. The fracture mechanism of the wheelset lifting apparatus is first clarified through the material analysis of macro/micro and dynamic stress tests. Static strength and modal analyses are then performed to perfect the mechanism analysis in terms of structural performance. An efficient, robust, fatigue design optimization method based on the double-layer Kriging surrogate model and improved non-dominated sorting genetic algorithm II (NSGA-II) is finally proposed to improve the original design scheme. For the wheelset lifting mechanism’s fracture, the crack source is found on the transition fillet surface of the lifting lug and lifting ring, where the fracture has the characteristics of two-way, multisource, high-cycle, low-stress fatigue. It is further revealed that the vibration fatigue occurring at the point of maximum stress is the main cause of the fracture. The effectiveness of the proposed design optimization method is validated via comparative analysis.

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