## 3 Experimental Results
### Experimental Results
#### 3.2.3 Experiment 3: Experiments on Additional Sensor Measurement Noises  
Under real flight conditions, vibration signals measured from the accelerometers are inevitably subject to measurement noise. In this experiment, we tend to evaluate the robustness of the proposed approach under the presence of measurement noise in vibration measurements. Though the acceleration measurements in the previous experiment in Sections 3.2.1 and 3.2.2 are from real accelerometers, these signals already contain some inherent measurement noise; in this experiment, additional noises are added to the acceleration measurements. In particular, we choose to use the data in Section 3.2.1, i.e., the random excitation. Gaussian noise with a mean of 0 and a standard deviation corresponding to 0-30% of the vibration amplitude, i.e., the maximum value of the random excitations, which is further added to the random vibration signals collected on the experimental setup. Table 6 gives the quantitative analyses and comparisons of the identification results under different strengths of measurement noises. To compare the quantitative results, we choose the second-best approach in Section 3.2.1, namely the 1D-att-CNN [25] approach, for comparisons. From Table 6, it can be seen that for both cases of symmetrical and asymmetrical sensor distributions, the proposed method outperforms in terms of both metrics when the additional measurement noise exists. Figure 7 shows the changes in the RMSE and CC metrics when the noise strengths increase. From Figure 7b, it can be seen that for symmetrical sensor distributions, the CC metric of approach in [25] would decrease dramatically from 0.9232 when no additional measurements noised to 0.2794 when 30% of the measurement noise. In the meantime, the proposed would remain above 0.75 at all noises strengths, which indicates more consistent identification results. For asymmetrical sensor distributions, though the approach in [25] can obtain a lower RMSE than that of the proposed approach when no additional measurement noises exists, when noises are added, the proposed approach outperforms in terms of the two metrics. Once again, the proposed would remain above 0.75 in CC at all noises strengths, which shows the robustness of the proposed method on additional measurements noises. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sensor Distribution** & **Load** & **Method** & **RMSE** & **CC** \\ \hline \multirow{6}{*}{Symmetric} & \multirow{2}{*}{\(5\sin(20\pi t)\)} & TSVD & 1.3486 & 0.9163 \\  & & 1D-att-CNN & 0.1421 & 1.0000 \\  & & ours & 0.5830 & 0.9999 \\ \cline{2-5}  & \multirow{2}{*}{\(5\sin(30\pi t)\)} & TSVD & 1.0771 & 0.9565 \\  & & 1D-att-CNN & 0.2218 & 1.0000 \\  & & ours & 0.3361 & 0.9999 \\ \hline \multirow{6}{*}{Asymmetric} & \multirow{2}{*}{\(4\sin(20\pi t)\)} & TSVD & 0.9681 & 0.9500 \\  & & 1D-att-CNN & 0.1581 & 1.0000 \\ \cline{1-1}  & & ours & 0.3572 & 0.9999 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{\(4\sin(30\pi t)\)} & TSVD & 0.7061 & 0.9814 \\ \cline{1-1}  & & 1D-att-CNN & 0.1909 & 1.0000 \\ \cline{1-1}  & & ours & 0.4451 & 0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Load identification performances comparisons on sinusoidal loads. For comparison, 1 regularization-based method, namely the TSVD regularization method, and 1 neural network-based method [25] are utilized for comparisons.