## 4 Experiment Validation
### Case 1: Bearing Fault Diagnosis Based on CWRU Bearing Datasets  
#### 4.1.1 Experiment Setup and Data Description  
The experimental equipment of CWRU is shown in Figure 5. It consists of a power meter, a torque sensor, an acceleration sensor, and a 2-horsepower motor. In the experiment, three sets of sensor data were obtained by placing the sensors at the motor supporting base and at the twelve o'clock direction of both the fan end (FE) and drive end (DE) of the motor housing. By electro discharge machining, 7, 14, and 21 mils diameter faults were introduced to SKF bearings. The faults were located in three, six, and twelve o'clock directions of the inner race, ball, and outer ring, respectively. The sampling frequency is 12 K and 48 K.  
Figure 4: The architecture of proposed method.  
The data used in the experiment are one of DE with the sampling frequency of 12 K. The ball pass frequency on the outer race (BPFO) of the experimental bearing is 104.56 Hz, ball pass frequency on the inner race (BPFI) is 157.94 Hz, and ball spin frequency (BSF) is 137.48 Hz. The selected fault types are inner race, ball, and outer race faults in the six o 'clock direction. The selected fault diameter is 7 and 14 mils diameter faults, and the motor load is 0 and 1 horsepower (HP). There are nine different failure types in the experiment. Each type of bearing failure can be seen in Figure 6. A total of 2700 samples were selected from three sets of sensor data with 1024 data as one sample, and the number of samples in each set of sensor data is 900. In order to verify the proposed method, three samples were selected from three sets of sensor data, respectively, and the three selected samples were merged into a matrix with three rows and 1024 columns. That is, the number of the new sample is 900, of which 80% were used for training and 20% for tests. A detailed and specific description of the data used in the experiment of CWRU is listed in Table 1.  
For the original multi-sensor data, the SAM is used to weight them first to obtain the time series data with more obvious features, which is to facilitate the subsequent convolution operation to extract more typical and representative features. As shown in Figure 7, the outer race fault data with no motor load and the fault diameter of 7 mils are selected to compare the time domain graphs of the unweighted and weighted. By comparing the time domain graphs composed of the first 1024 points of the data in Figure 7a,d, it can be observed that by weighting, the waveforms of the data with high  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Location** & \multicolumn{3}{c}{**Inner Race**} & \multicolumn{3}{c}{**Ball**} & \multicolumn{3}{c}{**Outer Race@6**} \\ \hline Diameter (mil) & 7 & 14 & 14 & 7 & 14 & 14 & 7 & 14 & 14 \\ \hline Label & 0 & 1 & 6 & 2 & 3 & 7 & 4 & 5 & 8 \\ \hline Motor Load (hp) & 0 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 \\ \hline Train & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 \\ \hline Test & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental datasets of CWRU.  
Figure 5: The experimental platform of CWRU.  
Figure 6: Bearing fault. (**a**) normal, (**b**) cage fracture, (**c**) inner race wear, (**d**) outer race wear.  
similarity in the signal become more unified, the characteristics of the signal become more obvious, and the overall waveform of the signal becomes more regular and symmetrical.