A hybrid Gaussian process model for system reliability analysis

• Published in: Reliability engineering & system safety Vol. 197; pp. 106816 - 15
• Main Authors: Li, Meng, Sadoughi, Mohammadkazem, Hu, Zhen, Hu, Chao
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.05.2020; Elsevier BV
• Subjects: Adaptive multivariate probability of improvement; Dependence; Gaussian process; Hybrid Gaussian process; Hybrid systems; Mathematical models; Multivariate analysis; Randomized dependence coefficient; Reliability analysis; Reliability engineering; System reliability; System reliability analysis; Adaptive multivariate probability of improvement; Randomized dependence coefficient; Hybrid Gaussian process; System reliability analysis
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2020.106816

•We propose a hybrid Gaussian process model for system reliability analysis.•This model is a hybrid of multivariate and univariate Gaussian process models.•Component interdependencies are quantified using a nonlinear-dependence measure.•Results of five case studies verify the effectiveness of the proposed method. This paper proposes a new system reliability analysis method based on a hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models. The proposed method is named hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of an engineered system according to their interdependencies: MGP models are constructed over the groups of highly interdependent components and UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to learn and quantify the pairwise dependencies between the components involving both linear and complex nonlinear dependency patterns. An initial hybrid Gaussian process (HGP) model comprising both UGP and MGP models is first constructed with a set of near-random samples and these UGP and MGP models are then updated with new samples that are sequentially identified based on a new acquisition function named adaptive multivariate probability of improvement. Results of three mathematical and two real-world engineering case studies suggest that the proposed HGP-SRA method can achieve better accuracy and efficiency in system reliability estimation than several benchmark surrogate-based methods.