An Efficient System Reliability Analysis Method Based on Evidence Theory With Parameter Correlations

With the ever-increasing complexity and scale of advanced modern engineering systems, multifailure modes coupling and input parameter correlations become important and inevitable challenges that hinder efficient reliability analysis of complex mechanical systems. To tackle this problem, in this article, a system reliability analysis method based on evidence theory considering parameter correlations is proposed. First, the optimal Copula function is selected by the Akaike information criterion using existing samples and the joint basic probability assignment considering parameter correlations is calculated. Second, engineering systems with multifailure modes are divided into series systems or parallel systems. The corresponding belief and plausibility measures of system reliability are derived, respectively. Moreover, support vector regression models are constructed by Latin hypercube sampling and genetic algorithm to replace the real performance functions. Therefore, the probability interval consisting of belief and plausibility measures is obtained through fewer performance function calls. Finally, two numerical examples and an engineering application of a 6-DoF industrial robot are exemplified to verify the effectiveness of the currently proposed method.