A new data fusion prediction model for low-fidelity and high-fidelity data on reliability evaluation based on joint parameters sampling

The high-fidelity data (HF) referring to the test data from real experiments can more accurately reflect the real performance indicators of the workpieces for reliability analysis in engineering. Due to the limited cost, enough HF data is difficult to be collected to meet the requirement of reliability analysis. Alternatively, a large amount of low-fidelity (LF) experimental data from simulation experiments can be integrated with HF data to achieve reliability estimates with high precision. Existing literatures have studied this problem and made some progress, but the model is rather complicated and the solving efficiency is limited. Therefore, a new data fusion prediction model on reliability evaluation is introduced by Gaussian process (GP) and Bayesian theory. The key idea is to describe the LF and HF response models, respectively, with the same regression parameter and GP correlation parameter. Furthermore, the joint parameters sampling is adopted to estimate the unknown parameters and predict the reliability based on the hybrid Markov chain Monte Carlo algorithm. It is demonstrated through an illustrative example on the Nonlinear oscillation workpiece that the proposed model and sampling methods are flexible and efficient.