## 5 Multi-Point Diagnosis Model of 2-D CNN  
Different from the 1-D CNN in the previous chapter, the convolution kernel shape of 1-D CNN is a 2-D tensor (\(m\), \(n\)), where \(n>1\), and the convolution kernel used by 2-D CNN is a 3-D tensor (\(m\), \(n\), \(l\)), where \(n>1\), \(l\geq 1\). The number of channels output by each convolutional layer is the number of the the convolution kernel. Therefore, 1-D CNN and 2-D CNN have different numbers of channels in each convolutional layer. The data of eight measuring points signal is combined into single-channel 2-D data (an image) and the 2-D convolution operation is used to process the image, as shown in Figure 6. The image data under four working conditions is the input of the model and the feature is extracted through 2-D CNN and then the failure mode is identified. The schematic of a 2-D CNN multi-measuring point fault diagnosis model is shown in Figure 7. Because the input data is a one-channel gray scale image, the input dimension becomes (3000, 8, 1). Next, each convolutional layer performs a 2-D convolution operation and the input data is converted into multi-channel 2-D data by computing with different convolution kernels. Then, through the fully connected layer, the features extracted by the convolutional layer are further compressed and pattern recognition is performed. The specific parameters of the network are provided in Table 3 below. Figure 6: Model input data under four working conditions. Figure 7: Reciprocating compressor valve fault diagnosis model of the 2-D CNN multi-measurement point. It can be seen from Table 3 that the model includes six 2-D convolutional layers (Conv2D), five maximum pooling layers (MaxPooling2D), and two fully connected layers. In order to reduce the amounts of parameters, the dimension of the convolution kernel is set to (3,3) and the convolution step size is 1. Next, the size of the pooling area of the pooling layer is designed as (2,1) and the pooling step is 2. The fully connected layer of the last layer uses softmax as the activation function, which is like the previous 1-D CNN model, but the remaining layers all use the ReLU function. Because the input image of this model is oblong and the image will be compressed after each convolution, the key features at the edge of the image may be lost during convolution. In order to prevent this problem and make better use of edge data, the input of each convolutional layer is filled [28], which is to fill 0 around the image, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Dimensions of Output Data** & **Number of Training Parameters** \\ \hline Input Layer & (None, 3000, 8,1) & 0 \\ Conv1D 1 & (None, 3000, 8,8) & 80 \\ Batch Normalization 1 & (None,3000, 8,8) & 32 \\ Conv1D 2 & (None, 3000, 8, 16) & 3216 \\ Batch Normalization 2 & (None, 3000, 8, 16) & 64 \\ MaxPooling1D 1 & (None, 1500, 8, 16) & 0 \\ Conv1D 3 & (None, 1500, 8, 32) & 4640 \\ Batch Normalization 3 & (None, 1500, 8, 32) & 128 \\ MaxPooling1D 2 & (None, 750, 8, 32) & 0 \\ Conv1D 4 & (None, 750, 8, 32) & 1056 \\ Batch Normalization 4 & (None, 750, 8, 32) & 128 \\ MaxPooling1D 3 & (None, 375, 8, 32) & 0 \\ Conv1D 5 & (None, 375, 8, 16) & 4624 \\ Batch Normalization 5 & (None, 375, 8, 16) & 64 \\ MaxPooling1D 4 & (None, 187, 8, 16) & (None, 187, 8, 8) \\ Conv1D 6 & (None, 187, 8, 8) & 1160 \\ Batch Normalization 6 & (None, 187, 8, 8) & 32 \\ MaxPooling1D 5 & (None, 93, 8, 8) & (None, 5952) \\ Tile layer & (None, 5952) & 0 \\ Dense 1 & (None, 128) & 761984 \\ Dense 2 & (None, 4) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 2-D CNN multi-measurement point reciprocating compressor gas valve fault diagnosis model structure. Figure 8: Padding diagrammatic sketch. An image is convolved with a convolution kernel of size \((a,b)\). When the fill amount is \(p\), the output image size is \((a+2p-f+1,b+2p-f+1)\). Here, the expectation is that the size of the image remains the same after convolution, so the parameters \(p=1\) and \(f=3\) are chosen to meet the requirements.