## 3 The Proposed Method
### View-Level Weight Module  
Considering the difference in importance of multi-view data, it is not enough to directly fuse the data, and an effective feature fusion mechanism is needed. The core of the Channel Attention Mechanism (CAM) is to model and learn the importance of different channel features according to the relationship between channel features. This study utilizes the CAM [20] module to allocate resources to calibrate the importance of each view. The model structure is shown in Figure 2. which mainly includes three parts: dimension compression module, excitation module and re-weighting.  
It is assumed that the input of the model is \(x^{v}\), and \(v\) represents the number of channels. The CAM model gets the weight vector of each channel through squeezing and excitation, and then maps it to the original feature channel to realize the selection of channel importance. The specific operation process is as follows:  
1. [label=(0)]
2. Squeeze: The CAM compresses feature maps along channel dimensions through global maximum pooling and global average pooling to obtain the global feature representation of each feature map and perceive the feature representation of each channel from a global perspective [21]. The average pooling feature and the maximum pooling feature are represented by \(F^{v}_{avg}=AvgPool(x^{v})\) and \(F^{v}_{\max}=MaxPool(x^{v})\).
3. Excitation: In the excitation operation, \(F^{v}_{avg}\) and \(F^{v}_{\max}\) are respectively sent into a two-layer neural network with a bottleneck structure to model the correlation between different feature channels and generate weights for each feature channel. MLP consists of two convolution layers, each of which uses a \(1\times 1\) convolution kernel to extract the relationship features between channels. The input and output of MLP are consistent through scaling parameter R. Then, the two features obtained are added together to  
Figure 2: Architecture of the CAM model.  
obtain the weight coefficient \(u^{v}\) through an activation function. The whole process is represented as follows: \[\begin{array}{ll}u^{v}&=\sigma(MLP(F^{v}_{avg})+MLP(F^{v}_{\max}))\\ &=\sigma(w_{1}(f(w_{0}(F^{v}_{avg})))+w_{1}(f(w_{0}(F^{v}_{\max}))))\end{array}\] (1) where \(f\) and \(\sigma\), respectively, represent the ReLU activation function and sigmoid function, and \(w_{0}\) and \(w_{1}\), respectively, represent the convolution kernel parameters in MLP. The value \(u^{v}\) represents the weight information of the \(v\)th channel, i.e., \(1\times 1\times v\).
3. Reweight: The obtained channel weight information \(u^{v}\) is mapped to the corresponding channel feature, and the original input feature \(x^{v}\) is recorrected to obtain a feature map \(\widetilde{x}^{v}\) with channel attention for further fault diagnosis. \[\widetilde{x}^{v}=x^{v}\otimes u^{v}\] (2) It has been widely pointed out in the literature that not all views of data contribute equally to the fault diagnosis task, so this study applies different weights to the reconstruction error for multiple views.  
The loss function of Equation (1) is modified from the original sigmoid function to the softmax function, so that the weight range representation is changed from the original two values of 0 or 1 to the range of [0, 1].