Analysis of complex system reliability with correlated random vectors. (July 2016)

• Record Type: Journal Article
• Authors: Fan, Wenliang; Yang, Pengchao; Ang, Alfredo H-S.; Li, Zhengliang
• Is Part Of: Probabilistic engineering mechanics. Volume 45(2016:Jul.)
• Page Start: 61
• Page End: 69
• Publication Date: 2016-07
• Subjects: System reliability -- Correlated random vectors -- Transformation -- Taylor series expansion -- Complete system failure process -- Fourth-moment method; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2016.03.004
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: The analysis of reliability of complex engineering systems remains a challenge in the field of reliability. It will be even more difficult if correlated random vectors are involved, which is generally the case as practical engineering systems invariably contain parameters that are mutually correlated. A new method for transforming correlated distributions, involving the Nataf transformation, is proposed that avoids the solution of integral equations; the method is based on the Taylor series expansion of the probability density function (PDF) of a bivariate normal distribution resulting in an explicit polynomial equation of the equivalent correlation coefficient. The required numerical results can be obtained efficiently and accurately. The proposed method for transformation of correlated random vectors is useful for developing a method for system reliability including complex systems with correlated random vectors. Based on the complete system failure process (originally defined as the development process of nonlinearity) and the fourth-moment method, the analysis of system reliability for elastic-plastic material avoids the identification of the potential failure modes of the system and their mutual correlations which are required in the traditional methods. Finally, four examples are presented – two examples to illustrate the potential of the new method for transformation of correlated random vectors, and two examples to illustrate the application of the proposed more effective method for system reliability. Highlights: A new method for transforming correlated distributions is proposed. An improved iterative approach for the equivalent polynomial equation is presented. System reliability based on the complete system failure process and moment method. System reliability with correlated parameters and its impacts on system reliability.