Reliability Assessment Using Discriminative Sampling and Metamodeling

Abstract

Reliability assessment is the foundation for reliability engineering and reliability-based design optimization. It has been a difficult task, however, to perform both accurate and efficient reliability assessment after decades of research. This work proposes an innovative method that deviates significantly from conventional methods. It applies a discriminative sampling strategy to directly generate more points close to or on the limit state. A sampling guidance function was developed for such a strategy. Due to the dense samples in the neighborhood of the limit state, a kriging model can be built which is especially accurate near the limit state. Based on the kriging model, reliability assessment can be performed. The proposed method is tested by using well-known problems in the literature. It is found that it can efficiently assess the reliability for problems of single failure region and has a good performance for problems of multiple failure regions. The features and limitations of the method are also discussed, along with the comparison with the importance sampling (IS) based assessment methods.

Review of Metamodeling Techniques for Product Design With Computation-Intensive Processes

“…Zou and colleagues developed an indicator response surface based method, in which IS is performed in a reduced region around the limit state (Zou et al 2002;Zou et al 2003). The authors recently developed an innovative sampling method which achieves impressive efficiency and accuracy for global optimization, multi-objective optimization, and reliability assessment (Shan and Wang 2004;Wang et al 2005;. With its original inspiration from (Fu and Wang 2002), this sampling method is space filling and reflects the goal of sampling; it is a more aggressive MCS method.…”

“…Zou and colleagues developed an indicator response surface based method, in which Monte Carlo Simulation is only performed in a reduced region around the limit state (Zou et al 2003). The authors recently developed a more flexible discriminative sampling method with high efficiency and accuracy (Wang et al 2005).…”

Reliability-Based Optimization Using Evolutionary Algorithms

“…The number of necessary samples can be somewhat reduced by using a more systematic sampling, e.g., Latin hypercube sampling [5], importance sampling [9], or directional sampling [10] (see also [4]). Wang et al [11] proposed a combination of sampling and meta-modeling. Their approach applies a discriminative sampling strategy, which generates more points close to the constraint function.…”

Reliability assessment using probabilistic support vector machines

“…In this approach, an approximation of the system responses is constructed based on a design of experiments. Adaptive sampling schemes to refine the approximations have also been developed (Bichon et al, 2007;Wang et al, 2005;Huang et al, 2006). While these methods can efficiently handle non-linear limit-state functions, they are limited by several other difficulties.…”