## 3 Results  
### Experiment Introduction  
In this study, the experimental data are collected from a self-built test rig. Figure 5 shows the configuration of the experimental test system, which is used to verify the feasibility of the proposed method. The entire system consists of a frequency converter, motor, shaft, test bearing, data acquisition device (model: PAK MKII SC42, B&K, Copenhagen,  
Figure 4: Overall technical flowchart of the CRAMNet. Figure 3: Multi-head attention mechanism module. Denmark), array microphone circular board, and array microphone sensors (model: BSWA MPA436, sensitivity: 15.5 mV/Pa, sampling frequency: 16,384 Hz). During the experiment, the motor drives the rotating shaft and the test bearing to rotate, and the array microphones collect the acoustic signals generated during the operation of the bearing in real-time. These signals are transmitted to the data acquisition device and then to the computer for analysis. The array microphones, which consist of multiple sensors, accurately locate the sound source and have high resolution and sensitivity, capable of capturing weak acoustic signals. This is extremely important for detecting early faults and subtle anomalies in bearings. The specific parameters of the test bearing are shown in Table 1. During the experiment, the motor speed was set to 1500 rpm. The experimental samples were extracted using a sliding window method, with a window length of 256 and a sample data length of 2048. Each fault sample quantity was 400, with 300 samples used to train the model and 100 samples used to test the model. The operating conditions of the rolling bearing mainly include normal, inner ring fault, outer ring fault, rolling element fault, cage fault, and composite fault. Detailed data are shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structural** & **Parameter Values** & **Structural** & **Parameter Values** \\
**Parameters** & & **Parameters** & **Label** \\ \hline Bearing type & UC205 & Contact angle & 0\({}^{\circ}\) \\ Outside diameter & 52 mm & The number of roller & 9 \\ Bone diameter & 25 mm & Width & 34.1 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structural parameters of rolling bearing. Figure 5: Test bench for bearing faults. In the experiment, five different types of bearing faults were created, including normal state (N), inner race fault (IF), rolling element fault (RF), outer ring fault (OF), and cage fault (CF), as shown in Figure 6. Each fault was introduced through precise mechanical damage in a laboratory environment. The authenticity of each fault type was further validated through vibration signal analysis in both the frequency domain and time-domain feature extraction techniques. This ensured the effectiveness of the dataset for fault diagnosis tasks. In the neural network training process, to improve the model's training accuracy, data augmentation was performed using rotation, translation, scaling, and cropping methods during the generation of two-dimensional image data. During the training process, dropout and batch normalization techniques were used to accelerate convergence and prevent overfitting. The dropout rate was set to 0.4, the optimizer chosen was Adam with a learning rate of 0.001, and the loss function was set to cross-entropy loss. The training batch size was set to 32, and the number of epochs was set to 100. The network was implemented using Python version 3.10.6, PyTorch version 1.11.0+cu113, CUDA version 11.3, and the CUDA device name was NVIDIA GeForce RTX 3080. Table 3 shows the detailed network parameters.