Selective transmit modeling framework of complex system reliability analysis considering failure correlation.

• STMF is developed for system reliability analysis considering failure correlation. • The joint optimum of empirical risk and expected loss is realized by MSM of STMF. • The correlation measurement hierarchy is established by MTM of STMF. • STMF is verified by analysis of turbine bladed disk. • STMF holds superior accuracy and efficiency in reliability analysis. To improve the efficiency and accuracy of complex system reliability analysis considering failure correlation, a selective transmit modeling framework (STMF) is proposed by integrating multi-stage selective modeling (MSM) strategy with multi-failure transmit modeling (MTM) architecture. In MSM, the modeling quality of multi-learner is discussed to establish the optimal functional relationship between multi-variable and multi-response by considering the suitability of modeling samples, and the fitting performance of copula functions is quantified to construct the optimal correlation model between multi-failure through distance measure. For MTM, the correlation sequence measurement structure of multiple modes is established to construct the joint reliability analysis model of complex structure by updating iteratively the margin distribution functions with the strongest correlation. Taking the turbine bladed disk as study object, the validation of the proposed STMF is verified by comparing with other methods. The analytical results of study case can be expressed as that (i) STMF holds the superior performance in modeling accuracy and time-consuming, at 2.6308 × 10-2 and 0.4638 s respectively; (ii) STMF also has the best property in simulation, separately at 2.2123 s in simulation efficiency and at 99.9899 % in simulation precision; (iii) the reliability 0.9876 of study case obtained by STMF is higher than the reliability 0.9855 that depends on the mutual independence assumption. The effort of this work is prominent to promote effectively the quantification of multi-failure correlation and the improvement of reliability analysis performance for complex systems. [ABSTRACT FROM AUTHOR]