Optimal Design of Validation Experiment for Material Deterioration

For the deterioration model of a material, it is crucial to design a validation experiment to determine the ability of the deterioration model to simulate the actual deterioration process. In this paper, a design method of a validation experiment for a deterioration model is proposed to obtain the experiment scheme with low cost and satisfactory credibility. First, a normalized area metric based on probability density functions for the deterioration model is developed for validation results quantification. Normalized area metrics of different state variables in an engineering system can be applied to a unified evaluation standard. In particular, kernel density estimation is used to obtain smooth probability density functions from discrete experimental data, which can reduce the systematic error of the validation metric. Furthermore, a design method for the validation experiment for the deterioration model is proposed, in which the number of experimental samples and observation moments in each experimental sample are design variables, while the credibility of the validation experiment is the constraint. For the experiment design, the problem with varying dimensions of design variables occurred in the optimal design. Thus, a collaborative optimization method using the Latin hypercube sampling was developed to solve this problem. Finally, the results of the two examples showed the characteristics of the proposed metric and also reflected the correlation between the design variables and experimental credibility.