## 5 Conclusions  
To monitor the low velocity impact load of the composite laminates, and to provide a basis for judging the degree of impact damage, aiming at the impact signal characteristics of the composite structure with variable thickness, using fiber grating sensing and monitoring technique, a low velocity impact energy recognition method based on whitening EMD algorithm is proposed by adapting thickness coefficient correction method, and the steps of low velocity impact energy recognition for the composite structure with variable thickness are given. The FBG impact energy monitoring system is built by using the fiber grating sensing technology. The impact energy feature extraction based on whitening EMD algorithm is carried out according to the characteristics of variable thickness composites structure. The impact energy feature sets of the impact signals on the variable thickness specimen at 1 J, 2 J, 3 J energy were established, and the energy recognition verification experiment was conducted.The results show:  
(1) An improved whitening EMD method was proposed which can effectively avoid modal aliasing of high-frequency intrinsic mode signals for variable thickness composites, improve the validity of impact eigenvalues, increase the accuracy of energy recognition, and better extract the impact energy feature of variable thickness composite laminate. (2) A low velocity impact energy recognition system for variable thickness composite structure was designed and built. The system ensures the effectiveness of the impact energy recognition method and has the advantages of reliability, stability, anti-interference, and distribution. (3) The performance of the proposed impact energy recognition method was verified by experiments. Experiments show that this method can accurately estimate the 1-3 J impact energy, of which the average error is 4.82%. The recognition result accords with the scope \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Impact Point**} & \multicolumn{2}{l}{**Recognition Error**} & \multicolumn{2}{l}{**Recognition Error**} \\  & \multicolumn{2}{l}{**of Sensor**} & \multicolumn{2}{l}{**Impact Point**} & \multicolumn{2}{l}{**of Sensor**} \\  & \multicolumn{2}{l}{**Combination (1,2,3)**} & & **Combination (4,5,6)** \\ \hline L1 & 14.33\% & L1 & 20\% \\ L2 & 15\% & L2 & 18.33\% \\ L3 & 12\% & L3 & 13\% \\ L4 & 13\% & L4 & 12.67\% \\ L5 & 5.5\% & L5 & 5.33\% \\ L6 & 5.5\% & L6 & 6.5\% \\ L7 & 9.5\% & L7 & 6.67\% \\ L8 & 9.33\% & L8 & 6.33\% \\ L9 & 4.67\% & L9 & 6.33\% \\ L10 & 7.5\% & L10 & 2\% \\ L11 & 2.33\% & L11 & 6\% \\ L12 & 9.33\% & L12 & 2.5\% \\ L13 & 0.5\% & L13 & 5\% \\ L14 & 11.5\% & L14 & 13.33\% \\ L15 & 9\% & L15 & 8.67\% \\ L16 & 10\% & L16 & 5\% \\ L17 & 6\% & L17 & 1.67\% \\ L18 & 8.33\% & L18 & 0.33\% \\ L19 & 20.5\% & L19 & 13.5\% \\ L20 & 8.67\% & L20 & 1.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recognition results comparison of sensor combinations. of engineering application. The thickness coefficient correction step (especially in large thickness areas) significantly improves the recognition performance. (4) Since the thickness difference of the specimen is not significant, after validating the effectiveness of the thickness coefficient correction method, the future work needs to focus on the relationship between the performance of the correction method and the thickness difference of the structure. (5) Next, point selection algorithm should be introduced to simulate the recognition results of various sensor combinations to determine the optimal layout. In addition, further improvement is needed for the recognition method to solve the problem of sensor reduction. Conceptualization, G.L. and Y.X. ; methodology, Y.X. ; software, T.Z. ; validation, G.L. and T.Z. ; formal analysis, G.L. ; investigation, T.Z. ; resources, G.L. and T.Z. ; data curation, G.L. and T.Z. ; writing--original draft preparation, G.L. ; writing--review and editing, Y.X. and T.Z. ; visualization, G.L. and T.Z. ; supervision, Y.X.