ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

Abstract

The random variables are always truncated in aerospace engineering and the truncated distribution is more feasible and effective for the random variables due to the limited samples available. For high-reliability aerospace mechanism with truncated random variables, a method based on artificial bee colony (ABC) algorithm and line sampling (LS) is proposed. The artificial bee colony-based line sampling (ABCLS) method presents a multi-constrained optimization model to solve the potential non-convergence problem when calculating design point (is also as most probable point, MPP) of performance function with truncated variables; by implementing ABC algorithm to search for MPP in the standard normal space, the optimization efficiency and global searching ability are increased with this method dramatically. When calculating the reliability of aerospace mechanism with too small failure probability, the Monte Carlo simulation method needs too large sample size. The ABCLS method could overcome this drawback. For reliability problems with implicit functions, this paper combines the ABCLS with Kriging response surface method, therefore could alleviate computational burden of calculating the reliability of complex aerospace mechanism. A numerical example and an engineering example are carried out to verify this method and prove the applicability.

Development of a High-reliability Model for Public Hospitals in Iran

“…Nowadays, it is challenging to definea high-reliability organization as well ascomprehendthe manner of foundation of such an organization. In fact, an organization with high reliability can only attain this position after experiencing several failures (Peng, Zhang, & Zhu, 2015). These organizations could be identified by providing answers to the question that follows: how often has this organization experiencedfailuresthatcould have resulted indisastrous consequences but were averted?…”