## 6 Sensitivity Analysis  
At the beginning of the accelerated degradation test scheme design, the parameter \(\gamma_{0},\gamma_{1},\gamma_{2},\sigma\) in the stress model is usually obtained based on prior data or engineering experience, or estimated by a small number of grope tests. However, there are usually some deviations between these values and the true values. Therefore, the sensitivity of the model parameters to the test scheme is discussed and analyzed.  
Assuming that the true value of model parameters is the initial value used in the calculation example, other conditions are consistent with the example, and the influence of parameter deviation on the optimization result of test scheme is analyzed. The deviation levels of each model parameter are 10%, 0%, and \(-\)10%, respectively, and there are 81 combinations of deviation of the four parameters. In order to reduce the amount of calculation and make the selected deviation combination representative, orthogonal test \(L_{g}(3^{4})\) is used to conduct deviation test, and RV function is selected as the evaluation basis. The expression is shown in Equation (32) and the calculation results are shown in Table 19.  
\[RV=\left|\frac{\left|M^{11}(\xi)\right|-\left|M^{11}(\xi)\right|^{*}}{\left|M^ {11}(\xi)\right|}\right|\times 100\% \tag{32}\]  
where \(\left|M^{11}(\xi)\right|\) is the objective function value obtained when the model parameters are constant; \(\left|M^{11}(\xi)\right|^{*}\) is the objective function value obtained after the change of model parameters.  
From the results in the Table 19 that the scheme obtained by the optimization method is not sensitive to the variation of model parameters' deviation (10%, 0, \(-\)10%), indicating that the method has good stability. From Table 19, the variation of parameters \(\gamma_{0}\) and \(\gamma_{1}\) has great influence on the deviation of the test scheme, especially when the parameters of \(\gamma_{0}\) and \(\gamma_{1}\) fluctuate at the same time, thus the influence is obvious. This is because the method proposed in the calculation example is based on the parameters for subsequent optimization.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(\gamma_{0}\) & \(\gamma_{1}\) & \(\gamma_{2}\) & \(\sigma\) & \(n\) & \(S_{1}\) & \(S_{2}\) & \(\Delta t\) & \(m\) & \(RV\) \\ \hline
10\% & 10\% & 10\% & 10\% & 10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 80.6\% \\
10\% & 0 & 0 & 0 & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 46.9\% \\
10\% & \(-\)10\% & \(-\)10\% & \(-\)10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 93.6\% \\
0 & 10\% & 0 & \(-\)10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 11.1\% \\
0 & 0 & \(-\)10\% & 10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 47.0\% \\
0 & \(-\)10\% & 10\% & 0 & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 59.3\% \\ \(-\)10\% & 10\% & \(-\)10\% & 0 & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 32.2\% \\ \(-\)10\% & 0 & \(-\)10\% & \(-\)10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 25.3\% \\ \(-\)10\% & \(-\)10\% & 0 & 10\% & [5 5 5 5] & [800 850 900 950] & [6000 6500 7000 7500] & 10 & [66 62 61 61] & 37.0\% \\ \hline \end{tabular}
\end{table}
Table 19: Optimization results under different combinations of model parameter deviations.  
Therefore, in order to reduce the evaluation error of the subsequent parameters, the value range of the main parameters of the model should be as small as possible before the test, so as to ensure that the optimized test scheme can obtain higher accuracy.