## 4 Experimental Verification and Analysis
### Comparative Analysis
#### 4.5.3 Comparison with Other Fault Diagnosis Methods  
In order to verify that the CMA-MCFCNN model has certain advantages in recognition performance compared with the current multi-view information fusion algorithms, four multi-view information fusion models were selected for comparison, two models were based on data-level fusion, and the other two models were based on feature-level fusion and decision-level fusion. The input data of the method used for comparison were as follows:  
1. A DCNN model with large convolution kernel size was proposed by [PERSON] et al. [10]., using raw vibration signal as input. In the experiment, 114 data points intercepted in the corresponding time points of the vibration signals from nine views were connected in series to form a sample with a length of 1026. 2. [PERSON] et al. [11] proposed a two-dimensional CNN model (2 DCNN) composed of two groups of \"convolutional layers + pooling layers\" stacked, and the vibration signals of nine views were stacked row by row to form a two-dimensional matrix as the input of the model. In the experiment, the samples of nine groups of vibration signals were stacked row by row to form a matrix size of 9 \(\times\) 1024. 3. [PERSON] et al. [15] proposed a feature-level fusion fault diagnosis method (CNN_EMD) based on CNN and EMD. This method fused 80 CNN features, 11 time domains and EMD features, and then trained a softmax classifier for fault diagnosis. In the experiment, a fast Fourier transform, time-domain feature extraction and empirical mode decomposition were performed on the vibration signals collected from the seventh view, and then the frequency spectrum obtained by the Fourier transform was input into the CNN model. 4. [PERSON] et al. [18] proposed an ensemble deep Convolutional Neural Network model with improved D-S evidence (IDSCNN), taking the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals as the input of model. In the experiment, FFT was performed on the vibration signals at the seventh and second view, respectively. The comparison results of all methods are listed in Table 5. The average diagnostic accuracy of the proposed CAM_MCFCNN model on the three constant-speed datasets A, B and C is 99.95%, 99.87% and 99.92%, respectively, and the average diagnostic accuracy on the mixed-speed dataset D is 98.89%. Compared with the second-highest DCNN, the diagnostic accuracy of the CAM_MCFCNN model on the four datasets was improved by 7.3%, 6.04%, 1.21% and 7.06%, respectively. The CNN_EMD model had the lowest accuracy. The possible reason for this is that the hand-extracted features were not adapted to the fault mode of this study. The diagnosis accuracy of the CNN_EMD model had the worst performance on dataset D, which indicates that the features are not sensitive to speed. In addition, this study also analyzed and compared the model training time and parameters of each method. IDSCNN adopts the method of ensemble branch network, which suffers from high complexity during the training process as well as testing. Compared with other methods, the proposed CAM_MCFCNN model has a simple structure, fewer parameters and a shorter training time. The experimental results show that the proposed multi-view information fusion model with view-level weights is effective.