## 3 Experimental Results
### Experimental Results
#### 3.2.4 Experiment 4: Experiments under Unknown Excitation Source Disturbance Conditions  
For real aerospace structures, multiple loads could be applied simultaneously at different positions. Besides the load to be identified, i.e., the target load, the other additional loads could introduce extra disturbances to the identification problem. To further validate the robustness of the proposed method under such disturbance conditions, in this experiment, an extra vibration exciter is attached on the GARTEUR model. Figure 8 gives the locations of the target exciter (for target loads), disturbance exciter (for disturbance loads) and the accelerometers for both symmetrical distribution and the asymmetrical distribution.  
As for the loads, in the experiment, the target load is the Gaussian white noise random load described in Section 3.2.1. The disturbance load, on the other hand, is configured as a Gaussian white noise random load with amplitudes of 0%, 40% and 100% relative to that of the target load. Table 7 and Figure 9 give the quantitative analyses and comparisons in different sensor distributions.  
Similar to Section 3.2.3, the comparisons are with the 1D-att-CNN method [25]. Table 7 and Figure 9 show that in various disturbance conditions, our proposed method outperforms in both RMSE and CC metrics. From Figure 9a,b, it can be seen that for symmetrical sensor distribution, the performance of time domain method [25] would decrease more than the proposed method when stronger disturbance occurs. Though the RMSE in Figure 9c is lower for [25] than ours when there is no disturbance, when the disturbance is stronger, our method outperforms. This indicates that our method can achieve superior identification results and reflects its robustness in the presence of other dynamic load disturbances.  
Figure 8: Two different distributions of the accelerometer positions, with disturbance exciters. One extra exciter is placed at a different location in each scenario, which would provide the disturbance loads with different amplitudes. (**a**) Symmetrical distribution of the accelerometers, together with the disturbance exciter. (**b**) Asymmetrical distribution of the accelerometers, together with the disturbance exciter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Distribution** & **Disturbance Amplitude** & **Method** & **RMSE** & **CC** \\ \hline \multirow{6}{*}{Symmetric} & 0\% & 1D-att-CNN & 0.7517 & 0.9232 \\  & & ours & 0.4824 & 0.9702 \\ \cline{2-5}  & 40\% & 1D-att-CNN & 1.0261 & 0.8599 \\  & & ours & 0.7915 & 0.9221 \\ \cline{2-5}  & 100\% & 1D-att-CNN & 1.2517 & 0.7628 \\  & & ours & 1.0468 & 0.8432 \\ \hline \multirow{6}{*}{Asymmetric} & 0\% & 1D-att-CNN & 0.8211 & 0.9701 \\  & & ours & 1.0579 & 0.9710 \\ \cline{2-5}  & 40\% & 1D-att-CNN & 3.4922 & 0.6465 \\ \cline{2-5}  & & ours & 1.8053 & 0.9180 \\ \cline{2-5}  & 100\% & 1D-att-CNN & 3.7184 & 0.6311 \\ \cline{2-5}  & & ours & 2.0576 & 0.8994 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Load identification performances evaluations and comparisons under unknown excitation source disturbance conditions. The evaluated loads are also the random loads in Section 3.2.1. An extra vibration exciter is applied on the GARTEUR model, performing Gaussian random excitations with different amplitudes. Once again, the proposed approach is compared with the second-best algorithm in Section 3.2.1.  
Figure 9: The comparisons of the RMES and CC metrics of different apporach under different disturbance conditions. The first row gives the results for symmetrical sensor distribution, while the second row gives those of the asymmetrical sensor distribution. (**a**) The RMSE for symmetrical distribution under different disturbance conditions. (**b**) The CC for symmetrical distribution under different disturbance conditions. (**c**) The RMSE for asymmetrical distribution under different disturbance conditions. (**d**) The CC for asymmetrical distribution under different disturbance conditions.