# Multi-Scale Convolutional Recurrent Neural Network for
## 3 MS-CRNN for DAE-Based Noise Removal and Fault Detection
### MS-CRNN for Bearing Fault Detection  
For the detection of defects, this paper proposes the MS-CRNN. The data extracted through the DAE arrives as input to the MS-CRNN in two dimensions. The input data are input to three convolutional layers, and each layer has a different convolutional kernel. Data features are extracted through each convolutional layer. Particularly, a small-sized convolutional kernel detects a high-frequency feature, and a large-sized kernel detects a low-frequency feature. This allows more robust features to be extracted and provides both low- and high-frequency information of the existing signal. Thereafter, it is connected and input to the LSTM layer to consider the time series characteristics. After passing through LSTM, the data are entered into a fully connected layer for classification. The architecture of the proposed DAE + MS-CRNN is shown in Figure 6.  
Data that have been denoised through the DAE were of size 20 \(\times\) 20. The input data were fed into a layer with three differently sized convolution kernels. The first convolutional layer had a 1 \(\times\) 1 convolutional kernel and underwent a total of three pooling processes. The second convolutional layer had a 3 \(\times\) 3 convolutional kernel and went through a total of two pooling processes. The third convolutional layer had a 5 \(\times\) 5 convolutional kernel and underwent a total of one pooling operation. As the first layer had a small kernel size, high-frequency features could be extracted, and in the second layer, low-frequency features and high-frequency features could be appropriately extracted. The third layer could extract features of a low-frequency region.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Output Size** \\ \hline Conv2D & 20 \(\times\) 20 \\ BatchNormalization & 20 \(\times\) 20 \\ MaxPooling & 10 \(\times\) 10 \\ Conv2D & 10 \(\times\) 10 \\ MaxPooling & 5 \(\times\) 5 \\ Conv2D & 5 \(\times\) 5 \\ UpSampling2D & 10 \(\times\) 10 \\ BatchNormalization & 10 \(\times\) 10 \\ Conv2D & 10 \(\times\) 10 \\ UpSampling2D & 20 \(\times\) 20 \\ Conv2D & 20 \(\times\) 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DAE architecture summary.  
Figure 5: Original data, data with noise added, and data with noise removed.  
Max pooling was used for pooling, and Relu was used as an activation function. Thereafter, the features extracted through combining were fed as input to the LSTM layer. At this time, data were input in a one-dimensional form, and the temporal characteristics could be grasped with the long/short-term memory of LSTM. The input data were passed through the LSTM and then to the fully connected layer for classification. The input data were finally classified through the Softmax activation function. Table 2 lists the overall architecture summary of DAE + MS-CRNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Output Size** & **Network** & **Connected to** \\ \hline Input\_Layer & (20 \(\times\) 20) & Conv2D & \\
1 Conv\_Layer1 & (20 \(\times\) 20) & Conv2D, kernel size = 1 \(\times\) 1 & Input\_Layer \\
1 Pool\_Layer1 & (10 \(\times\) 10) & Maxpooling2D size = 2 \(\times\) 2 & 1C\_L1 \\
2 Conv\_Layer1 & (20 \(\times\) 20) & Conv2D, kernel size = 3 \(\times\) 3 & Input\_Layer \\
1 Conv\_Layer2 & (10 \(\times\) 10) & Conv2D, kernel size = 1 \(\times\) 1 & 1P\_L1 \\
2 Conv\_Layer2 & (20 \(\times\) 20) & Conv2D, kernel size = 3 \(\times\) 3 & 2C\_L1 \\
1 Pool\_Layer2 & (5 \(\times\) 5) & Maxpooling2D, size = 2 \(\times\) 2 & 1C\_L2 \\
2 Pool\_Layer1 & (5 \(\times\) 5) & Maxpooling2D, size = 4 \(\times\) 4 & 2C\_L2 \\
1 Conv\_Layer3 & (5 \(\times\) 5) & Conv2D, kernel size = 1 \(\times\) 1 & 1P\_L2 \\
2 Conv\_Layer3 & (5 \(\times\) 5) & Conv2D, kernel size = 3 \(\times\) 3 & 2P\_L1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the MS-CRNN architecture.  
Figure 6: Architecture of the DAE + MS-CRNN.