# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Experimental Platform  
The experiments are carried out based on the PT700 motor bearing platform as shown in Figure 5. The experimental platform is mainly composed of a three-phase asynchronous AC motor, a parallel gearbox, a bearing assembly, a coupling, a frequency converter, an oil level gauge, and an electromagnetic powder brake for changing loads. Considering the advantages and characteristics of different information sources of bearings and the complementarity between multi-source heterogeneous information of bearings. The vibration acceleration sensor is installed in the experimental platform as an external sensor, and the current sensor is used as a built-in sensor. In addition, the type of rolling bearing placed in the experimental platform is UC206, and the contact angle of the rolling element of the bearing is 0 degrees. To simulate the working state of the faulty bearing and consider the independence of different fault data of the bearing, the electrical discharge machining (EDM) technology was used in the experiment to process local single-point damage on the UC206 bearing artificially. The locations of local single-point damage are respectively located on the bearing's outer ring and inner ring, and the damage diameter is 0.3 mm, as shown in Figure 6. Thereby simulating three different bearing operating states: normal operating state, inner ring fault state, and outer ring fault state.  
The vibration datasets of the bearing and current datasets are collected synchronously through the VAL-DC29, and VAL-DC25 signal acquisition and analyzers under the condition that the motor speed and load are fixed at 1500 rpm and 0 hp, respectively. The operating state of the bearing is constantly changing. The number of data samples under different bearing operating conditions was balanced during the data collection process. The bearing data set is divided into a training set and a test set at a ratio of 7:3. The training set is used to train a fault diagnosis model. The test set is used to evaluate the relevant performance of the model. Among them, the number of samples in the training set is 1680, and the number of samples in the test set is 720. To ensure that the collected current and vibration data samples include the periodic characteristics and fault information characteristics of one revolution of the bearing, the number of sampling points for one revolution under test as the number of sampling points contained in one sample in the experiment. According to the calculation of the transmission ratio formula and the highest analysis frequency formula, it is finally obtained that the bearing can sample about 512 points per revolution; that is, every 512 continuous sampling points are selected as an experimental sample. The details related to data collection are summarized in Table 2.  
Figure 5: Experimental platform.  
Figure 6: Bearing with a diameter of 0.3 mm.  
Figure 7 shows the time-domain waveform of the vibration signal and current signal per unit sample length under the normal bearing state, the fault state of inner and outer rings.