Failure probability estimation of a class of series systems by multidomain Line Sampling

This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic thresholds. We propose multidomain Line Sampling as an extension of the classical Line Sampling to work with a large number of components at once. By taking advantage of the linearity of the performance functions involved, multidomain Line Sampling explores the interactions that occur between failure domains associated with individual components in order to produce an estimate of the failure probability. The performance and effectiveness of multidomain Line Sampling is illustrated by means of two test problems and an application example, indicating that this technique is amenable for treating problems comprising both a large number of random variables and a large number of components.

Automated summary

This paper proposes an approach for assessing the failure probability of a particular class of series systems, using multidomain Line Sampling to explore interactions between components for producing failure probability estimates. The effectiveness of this technique is demonstrated through test problems and an application example, showing its suitability for dealing with problems involving a large number of random variables and components.