# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Performance Evaluation in the Complementarity of Multi-Source Heterogeneous Information  
To evaluate the complementarity among multi-source heterogeneous information of industrial motor bearings and the influence of multi-source heterogeneous information on the performance of the fault diagnosis model, the synchronously collected bearing vibration signal and current signal data are respectively input into the local fault diagnosis model based on Bi-LSTM and denoted them as Bi-LSTM-V algorithm and Bi-LSTM-C algorithm. The Bi-LSTM-V and Bi-LSTM-C algorithms are set as comparison algorithms. To control a single variable, the training hyperparameters of the local fault diagnosis model and the number of neurons in each network layer are kept consistent. The experiments are repeated several times to reduce the impact of experimental contingency on fault diagnosis accuracy.  
Figure 8 shows the fault diagnosis accuracy of the Bi-LSTM-V, Bi-LSTM-C, and MLMF-CR algorithms. The MLMF-CR algorithm proposed in this paper has obvious advantages in fault diagnosis accuracy than the other two algorithms. In the six times of experiments, the fault diagnosis accuracy of the MLMF-CR algorithm far exceeds that of the Bi-LSTM-V and the Bi-LSTM-C algorithms, which shows that compared with single-source information, multi-source heterogeneous information of bearings has more comprehensive and state information and is more helpful for bearing fault diagnosis. It is worth noting that the average fault diagnosis accuracy rates of the MLMF-CR, the Bi-LSTM-V, and Bi-LSTM-C algorithms are 98.58%, 96.43%, and 94.85%, respectively. Although the average fault diagnosis accuracy of the Bi-LSTM-V and Bi-LSTM-C algorithms is far lower than that of the proposed algorithm in this paper, they can achieve good results, which shows that the fault diagnosis model based on Bi-LSTM can still carry out effective fault identification when the input is single information source data. In addition, it can also be seen that when the Bi-LSTM-C algorithm is used for bearing fault diagnosis, its fault diagnosis accuracy index is lower than that of the Bi-LSTM-V algorithm. This may be because the test bearing of the experimental platform used in the experiments is installed outside the motor. At this time, the current source signal is more likely to be affected by factors such as its generation mechanism and propagation path, which leads to the higher accuracy of the state identification results based on vibration source data. When the vibration data and current data of the bearing are comprehensively used for fault diagnosis, the fault diagnosis accuracy rate of a single experiment and the average fault diagnosis accuracy rate  
are improved, which also confirms from the side that the bearing vibration data (external sensor data) and current data (built-in sensor data) has a certain complementary effect.  
Figure 9 shows the F1-Score index of the algorithm under different bearing conditions. Among them, the F1-Score index of the proposed MLMF-CR algorithm is better than that of the Bi-LSTM-C and Bi-LSTM-V algorithms, regardless of the status of the bearing. Moreover, the F1-Score index change fluctuation is also relatively small in the proposed algorithm under the different operating statuses of the bearing, which fully demonstrates that the MLMF-CR algorithm has relatively excellent comprehensive performance. The Bi-LSTM-V algorithm has also achieved relatively satisfactory results regarding F1-Score indicators, and the index changes in different status are relatively stable. In contrast, the F1-Score index of the Bi-LSTM-C algorithm fluctuates greatly under different bearing status conditions, and the F1-Score index value under different operating states is also weaker than the other two algorithms. In summary, the numerical results demonstrate the importance of multi-source heterogeneous monitoring data for bearing fault diagnosis.