## 3 Simulation Signal Analysis
In the EMD decomposition method, mode mixing is a problem observed in components IMF2-IMF6, where they influence each other and are difficult to identify distinctly. However, the improved CEEMDAN decomposition effectively resolves the issue of mode mixing in the obtained components. The improved CEEMDAN method has the highest overall kurtosis level after decomposition, indicating the inclusion of relatively more effective signal components. The components with the top two kurtosis values are IMF4 and IMF5, while the overall kurtosis value of CEEMDAN is lower than that of the improved  
Figure 2: Simulation Signal Time-Domain Diagram. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decomposition** & \multirow{2}{*}{**Decomposition Order**} & \multicolumn{2}{c}{**Cross-Correlation Coefficient**} & \multirow{2}{*}{**Kurtosis**} & \multirow{2}{*}{**Sample Entropy**} \\
**Algorithm** & & & & \\ \hline  & IMF1 & 0.0284 & 2.1772 & 1.2447 \\  & IMF2 & 0.0134 & 2.8909 & 1.1447 \\  & IMF3 & 0.0179 & 2.9157 & 0.7159 \\ EMD & IMF4 & 0.0020 & 3.2369 & 0.5941 \\  & IMF5 & 0.0017 & 3.8646 & 0.4715 \\  & IMF6 & \(-\)0.0191 & 2.8485 & 0.2547 \\ \hline  & IMF1 & 0.0377 & 2.2205 & 1.4206 \\  & IMF2 & 0.0190 & 2.8286 & 1.3053 \\ EEMD & IMF3 & 0.0073 & 3.0285 & 0.7125 \\  & IMF4 & 0.0015 & 3.1341 & 0.6053 \\  & IMF5 & 0.0184 & 3.3933 & 0.4845 \\  & IMF6 & \(-\)0.0024 & 2.8040 & 0.2774 \\ \hline  & IMF1 & 0.0406 & 3.1639 & 2.1710 \\  & IMF2 & 0.0363 & 2.3427 & 1.5538 \\  & IMF3 & 0.0109 & 2.8680 & 1.1767 \\ CEEMD & IMF4 & \(-\)0.0002 & 2.9873 & 0.6512 \\  & IMF5 & 0.0216 & 3.0404 & 0.5448 \\  & IMF6 & 0.0052 & 2.8592 & 0.3443 \\ \hline  & IMF1 & 0.0333 & 2.2353 & 1.4703 \\  & IMF2 & 0.0202 & 2.8792 & 1.2438 \\  & IMF3 & 0.0105 & 3.1252 & 0.6793 \\ CEEMDAN & IMF4 & 0.0009 & 3.1209 & 0.6111 \\  & IMF5 & 0.0204 & 3.3664 & 0.4899 \\  & IMF6 & \(-\)0.0190 & 2.5921 & 0.2662 \\ \hline  & IMF1 & 0.0344 & 2.2465 & 1.4909 \\  & IMF2 & 0.0208 & 2.8736 & 1.2156 \\ Improving & IMF3 & 0.0051 & 3.1151 & 0.6980 \\ CEEMDAN & IMF4 & 0.0043 & 3.1617 & 0.5805 \\  & IMF5 & 0.0202 & 3.4685 & 0.4604 \\  & IMF6 & \(-\)0.0195 & 2.5430 & 0.2170 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of various indexes after simulation signal decomposition. Figure 4: A comparison of magnitude spectrum between Improved CEEMDAN and CEEMDAN decompositions. It can be seen through the magnitude spectrum and power spectrum that the IMF3 after CEEDMAN decomposition has a higher magnitude in the interval of 0-4000 Hz, with worse overall concentration; IMF5 has a higher magnitude in the interval of 0-1000 Hz, with better overall concentration. While IMF4 after the decomposition of the improved algorithm has a high amplitude in the interval of 0-2000 Hz with a good overall concentration, IMF5 has a high amplitude in the interval of 0-1000 Hz with a high overall concentration. Overall, the amplitude concentration and energy concentration of the highest crag IMF of the improved algorithm are better than that of CEEMDAN, and although there are a large number of noise signals, the overall spectral characteristics of the improved algorithm after decomposition are obvious, and the interference frequency is better suppressed, which is conducive to the further identification of fault characteristics. Through the analysis above, it can be seen that the improved CEEMDAN algorithm can decompose the signal more effectively than the other methods of adaptive decomposition, and at the same time reduce the false components and improve the decomposition accuracy. In order to further validate the performance of the proposed improved CEEMDAN algorithm with SVD joint noise reduction, further noise reduction is carried out on all orders of IMFs of the simulation signals extracted from combine harvester assembly quality problems using the improved CEEMDAN method.