## 3 Results  
### Experimental Introduction  
To verify the effectiveness of the proposed method, data collection was carried out on the fault test bench of a rolling bearing in the anechoic chamber. The test bench consisted of a fault bearing, bearing seat, shaft, motor, data collector, and acoustic array sensor. The test device is shown in Figure 5. The model of the data collector is PAK MKII-SC42. The measuring devices consist of 16 BSWA MPA416 acoustic array sensors; the sensitivity of each sensor varies from 48.5 mV/Pa to 50 mV/Pa; the frequency response range of the device is 20-20,000 Hz; the temperature range is \(-10\)\({}^{\circ}\)C to 50 \({}^{\circ}\)C; the pressure coefficient is \(-0.06\) dB/kPa. The sampling frequency is 20 kHz, the plane where the sensor is located is perpendicular to the shaft, and the distance from the tested bearing is 300 mm. The acoustic array sensor and data collector are shown in Figure 6a,b, respectively. Figure 4: General technology roadmap. The specific geometric parameters of the test bearing are presented in Table 1. In order to accurately simulate the operational state of a faulty bearing and account for the independent fault data of each component, wire-cutting technology was employed to manually induce faults in the NU205 bearing. The damage locations were deliberately introduced in the outer ring, inner ring, rolling elements, and bearing cage with a consistent damage width of 0.1 mm. The experiment involved testing and analyzing a total of seven different bearing health states, including the inner ring fault, outer ring fault, rolling element fault, cage fault, and coupling fault. Each fault was installed separately on the test bench for analysis. Figure 7 provides a visual representation of the defective components in the bearings. For each health condition, 600 training samples were available for analysis; 400 data samples were used to train the network model, while the remaining 200 samples were utilized for model testing. To ensure fairness in experimentation, all comparison results underwent analysis using a 5-fold crossover method. The length of each sample data was set at 2048 units with a motor speed of 980 RPM. Additionally, digital labels ranging from 0 to 6 accurately represented different types of faults assigned to them. Further details regarding the sample dataset can be found in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Structural Parameter** & **Parameter Value** & **Structural Parameter** & **Parameter Value** \\ \hline Bearing type & NU205 & Contact angle & 0\({}^{\circ}\) \\ Inside diameter & 25 mm & The number of roller & 13 \\ Pitch diameter & 43.8 mm & Outside diameter & 52 mm \\ \hline \end{tabular}
\end{table} 
Table 1: Structural parameters of rolling bearing. Figure 5: Test bench. Figure 6: (a) Acoustic array sensors, (b) data collectors. Table 3 shows the network framework of the algorithm proposed in this paper. In the mathematical morphological layer, the SE length was set to 8, and the maximum scale of structural elements was set to 4. The batch sizes for the training and test samples were set to 36, the optimizer to Adam, the learning rate to 0.001, and the iteration step size to 100. To prevent overfitting, batch normalization (BN) and dropout were adopted, with a value of 0.6. The hardware configuration value was calculated: 11 th Gen Intel(R) Core(TM) [EMAIL_ADDRESS] GHz and NVIDIA GeForce GTX 3070 GPU. The Python language possesses the advantages of object orientation, high development efficiency, extensibility, and strong embeddability. Therefore, the MMNet was built under Torch-gpu 1.11.0 based on Python 3.10.