## 4 Experiment
### Real Signal Experiment
#### 4.3.3 Performance Analysis of Signal Reconfiguration Schemes  
Signal reconstruction experiments were conducted by feeding the same compressed measurement signal \(y\) into the trained ISTA-Net, ADMM-1 DNet, and ADMM + K-SVD algorithm methods. (For fairness, all reconstruction methods operate independently on the same computer by comparing the evaluation indexes in Section 4.1). Figure 14(a) plots the time-frequency diagram of the CWRU dataset BA fault signal after noise addition, and Figure 16 shows the corresponding signal reconstruction results of the three algorithms at different compression rates. It can be observed that the ADMM-1 DNet network can maintain an extremely low restructuring \(T_{\text{test}}\) even at 20% high \(r\). The reconstruction \(T_{\text{test}}\) of the K-SVD method decreases with the increase in \(r\), but the value is still much higher than that of the method proposed in this paper. The ISTA-Net network has unstable reconstruction results with increasing \(r\); it has a lower \(T_{\text{test}}\) for reconstruction only at a certain \(r\) and is still numerically higher than the method proposed in this paper. Table 4 records the reconstructed PSNR and Time values. Under the same \(r\), the ADMM-1 DNet network achieves the highest PSNR value, and the algorithm has the fastest convergence speed, while other classical algorithms can only show high performance at a low compression rate. This demonstrates that the proposed network fully combines the advantages of the ADMM algorithm, deep neural network, and redundant analysis operator and significantly improves algorithm reconstruction accuracy and convergence speed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{C\_R} & \multicolumn{3}{c}{**ISTA-Net**} & \multicolumn{3}{c}{**ADMM + K-SVD**} & \multicolumn{3}{c}{**ADMM-1 DNet**} \\ \cline{2-10}  & PSNR (dB) & **Time (S)** & \(T_{\text{gen}}\) & **PSNR (dB)** & **Time (S)** & \(T_{\text{gen}}\) & **PSNR (dB)** & **Time (S)** & \(T_{\text{gen}}\) \\ \hline
10\% & 21.9046 & 1.7225 & 0.0107 & 17.9538 & 3.6527 & **—** & 27.6102 & **0.6467** & 0.00120 \\
15\% & 22.3762 & 1.3144 & 0.0082 & 18.1029 & 3.6453 & **—** & 28.3691 & 0.7308 & 0.00175 \\
20\% & 23.4711 & **1.2104** & 0.0056 & 18.2139 & 3.6291 & **—** & 31.4135 & 0.6708 & 0.00051 \\
25\% & 22.5738 & 1.3024 & 0.0093 & 19.5731 & 3.5562 & **—** & 32.5003 & 0.6833 & 0.00048 \\
30\% & 23.7329 & 1.3991 & 0.0053 & 22.5722 & 3.5421 & **—** & 33.0522 & 0.7117 & 0.00041 \\
40\% & 24.8563 & 1.3667 & 0.0048 & 24.3601 & **3.5409** & **—** & 35.8264 & 0.7608 & 0.00022 \\
50\% & **27.5307** & 1.2941 & **0.0041** & **25.4638** & 3.5527 & **—** & **36.6274** & 0.6717 & **0.00016** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of PSNR (dB), Time, and \(T_{\text{gen}}\) results for three algorithms reconstructing the BA signal of the CWRU dataset at different compression rates. The best performance is marked in bold, and the second-best performance is underlined. Time is a running time record for the three methods, showing the average time used for 100 iterations of 1024 samples. Figure 15: Time domain diagram of noisy signal: **(a)** CWRU-BA; **(b)** XJTU-SY-MIX. The MIX fault signal of the XJTU-SY dataset with higher signal complexity is selected as the reconstruction object. Figure 15b plots its noise-adding time-frequency diagram. Figure 17 shows the reconstruction results of the three algorithms under different compression rates. It can be observed that when facing complex signals, the ISTA Net network is almost unable to complete reconstruction at compression rates of 25% and 30%, but the \(T_{test}\) of the ADMM-1 DNet network is still significantly lower than the other two algorithms. In addition, the PSNR and Time values of the three algorithms under different compression rates are recorded in Table 5. It can be seen that the ADMM-1 DNet network has a high reconstruction accuracy, while the PSNR and Time (t) of the reconstructed signals are better than the baseline algorithms.