## 7 Experimental Verification
### Signal Noise Reduction  
To evaluate the noise reduction effectiveness of the LVY-CYCBD algorithm proposed in this paper, we analyze the first set of sampled signals from Bearing 3_1. The time-domain waveform and the envelope spectrum of the vibration signal in the horizontal direction are shown in Figure 6.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter Name** & **Bearing Diameter (mm)** & **Ball Diameter (mm)** & **Number of Balls** & **Contact Angle (\({}^{\circ}\))** \\ \hline Value & 34.55 & 7.92 & 8 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of bearings.  
Figure 5: Test platform.  
Figure 6: Bearing 3_1 signals.  
From the time-domain waveform, it can be observed that the signal contains significant noise, making it difficult to identify distinct periodic pulse features. In the envelope spectrum, there are numerous interference spectral lines, and the spectrum appears cluttered, indicating a high noise content in the signal.  
The proposed LVY-CYCBD algorithm is applied to denoise the signal. Compared with commonly used optimization algorithms, the IVY algorithm demonstrates significant advantages in performance. This study compares it with the Particle Swarm Optimization (PSO) algorithm and the Sparrow Search Algorithm (SSA). Figure 7 illustrates the convergence trends of the three methods. As shown in the figure, the IVY algorithm achieves the optimal fitness value in the fourth iteration. Compared with the other two algorithms, the IVY algorithm not only converges the fastest but also achieves the highest convergence accuracy.  
In this study, the LVY optimization algorithm was used to optimize two key parameters in the CYCBD method: cyclic frequency and filter length. Through experiments, the optimal cyclic frequency was determined to be 114 Hz, and the optimal filter length was 85. These parameters were adopted as the final LVY-CYCBD algorithm settings. The LVY-CYCBD algorithm was compared with the CYCBD algorithm without parameter optimization (cyclic frequency of 90 Hz and filter length of 100). The time-domain waveform and envelope spectrum obtained from processing the experimental signals are shown in the Figures 8 and 9.  
As shown in the figure, compared to the original method, both the LVY-CYCBD algorithm and the traditional CYCBD algorithm effectively suppress noise interference in the signal, making the periodic pulse components more prominent. Additionally, a comparison of the envelope spectrums of the two methods reveals that the peak value of the envelope spectrum after denoising with the LVY-CYCBD algorithm does not exceed 0.00005 m/s\({}^{2}\), which is significantly lower than that of the unoptimized CYCBD algorithm. In terms of both noise amplitude and peak amplitude characteristics, the LVY-CYCBD algorithm demonstrates substantial improvement. This indicates that the LVY-CYCBD algorithm effectively reduces signal noise, enabling the extracted bearing characteristic frequencies to become clearer and more informative.  
To more intuitively evaluate the performance of denoised and reconstructed signals, this study compares the denoising effectiveness of the CYCBD, LVY-CYCBD, and CEEM-DAN algorithms using the signal-to-noise ratio (SNR) metric. The specific results are shown in Table 2. The results indicate that the LVY-CYCBD algorithm achieves a notable improvement in SNR compared to the other algorithms.  
Figure 7: Convergence trend diagram.