# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 3 Experimental Validation  
### Dataset Description  
To confirm the performance of the proposed model in bearing fault diagnosis, a laboratory-made bearing fault test platform was used to collect multiple faults and vibration signals at various levels. As shown in Figure 6, the test platform included variable frequency motors, normal bearings, test bearings, loading systems, and acceleration sensors. The test bearing model was 6205-2 RS SKF, and specific bearing parameters are listed in Table 2. Single-point or compound faults were set on the bearing surface by wire cutting. Figure 7 shows the real pictures of four bearings with four different health conditions. Types of faults included outer-race faults, ball faults, inner-race faults, inner-race compound ball faults, outer-race compound ball faults, and inner-race compound outer-race faults (IO). In this table, IB indicates that inner-race fault and ball faults exist at the same time on the test bearing, both under a fault degree of 0.2 mm. In the same way, IO indicates that inner-race fault and outer-race fault exist at the same time, and OB indicates that outer-race fault and ball fault exist at the same time. Specific data sets are described in Tables 3 and 4; dataset1 was collected under 0 kN load and dataset2 was collected under 1 kN load. The motor speed was set to 961 rpm, signal sampling frequency was set as 10 kHz, and the entire experiment was under a load of zero condition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Rate** & **Batch Size** & **Epoch** & **Number of Layer** \\ \hline
0.001 & 200 & 20 & 2 \\ \hline Size of Convolution & Size of Convolution & Size of Pooling & \\ Kernel in the 1 st Layer & Kernel in the 2 nd Layer & Kernel in the 2 nd Layer & Pooling Method \\ \hline
7 \(\times\) 7 & 5 \(\times\) 5 & 2 \(\times\) 2 & Maximum pooling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters of the network. Figure 5: Bearing fault diagnosis model based on an improved convolutional deep belief network (CDBN). FFT; fast Fourier transform. Figure 6: Bearing fault diagnosis model based on an improved CDBN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Contact Angle** & **Roller Diameter** & **Number of Rollers** \\ \hline \(0^{\circ}\) & 7.938 mm & 9 \\ \hline Outer Diameter & Internal Diameter & Pitch Diameter \\ \hline \(52\) mm & 25 mm & 38.5 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the test bearings. Figure 7: The real pictures of four bearings with four different health conditions. The original vibration signal was intercepted into samples with a length of 1024. From each label, 300 samples were obtained. Two-thirds of the total samples were used as training data, and the remaining third was used as testing data. The raw data of the bearing vibration signals is shown in Figure 8. It can be observed that the signal is noisy and lacks key information. The first step of the data preprocessing is to fold it into the size of 32 \(\times\) 32 by FFT. Figure 9 shows the state of 13 sample types after FFT processing and folding. The second step of data preprocessing is filtering and normalization, for the purpose of filtering out noise. Figure 10 shows the samples after band-pass filtering preprocessing. The figures clearly show that the noise was reduced without weakening the fault signal. By contrast, the texture features of the samples were enhanced, which is more conducive for convolutional neural networks to extract features.