Fuzzy Random Reliability Analysis Method

On the basis of the available research, the fuzzy-random reliability models are studied intensively in this contribution. The main innovative points are listed as follows. ① The A truncated subset method is presented for fuzzy reliability analysis of structural/mechanical system with fuzzy stress and fuzzy strength. The possible form of probability density distribution introduced on the truncated subset is discussed, and the effect of the distribution parameters on the fuzzy failure probability is analyzed.② For reliability analysis of complex structure/mechanism with inutile basic variables, a general response surface method, which takes the fuzziness and randomness in the basic variables into account simultaneously, is presented on the basis of equivalent transformation from fuzzy variables to random variables. In the presented method, the fuzziness and the randomness on the safe/failure state considered as well. The application on the general reliability analysis of elastic linkage mechanism shows the feasibility and efficiency of the proposed method in the engineering.③ Advanced fuzzy linear regression models with fuzzy input basic variables and fuzzy output response variables are presented on the basis of the available TANAKA regression models. By use of the proposed method, the fuzzy characteristics of the basic variables are transformed to the response variables. In that way, complex reliability analysis with multiple fuzzy variables is simplified greatly by the advanced fuzzy linear regression. The rationality and feasibility of the presented model is illustrated by the examples.④ For general reliability analysis with fuzzy basic variables and fuzzy failure domain, an improved numerical simulation algorithm is presented on the basis of simulated annealing optimization. By use of the presented method, the sampling efficiency and the calculate precision are both improved compared with the conventional method. Illustrations are used to explain the rationality and feasibility.⑤A truncation model is presented for the fuzzy variable with the non-closed membership function. The influence of the truncation parameters on the general failure probability is discussed. The proposed method can be extended to the structure/mechanism with multiple variables.⑥Finally, the thesis is summarized, and the further research works about the fuzzy-random reliability analysis are pointed out.