基于多源异构信息的航空发动机转子参数校准与可靠性分析

The accuracy of reliability analysis results of complex systems is closely related to the accuracy of input parameters. A stochastic model correction and parameter calibration method based on Bayesian maximum entropy is proposed to solve the reliability analysis problem containing multi-source uncertain information. By converting multisource statistical information (such as moment information and reliability) into constraint conditions, this method transforms parameter estimation into uncertainty optimization problem. Further considering the mixed uncertainty, Wasserstein distance is introduced to construct the likelihood function, and the approximation algorithm is used to improve the computational efficiency. This method extends the application scope of classical Bayesian inference by adding "entropy term" and can deal with multi-source heterogeneous data and mixed uncertainty problems. A multi-state system reliability model based on survival signature was established for a multi-component aero-engine rotor system, and the reliability analysis was carried out by using the above method. Through comparative analysis, it was verified that the proposed method has higher accuracy and stronger robustness than the traditional method.