# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Performance Evaluation in the Effectiveness of Decision Fusion of Multiple Partial Models  
To verify the validity of the effectiveness of the proposed MLMF-CR algorithm, we select three comparison methods from the feature-level information fusion fault diagnosis methods, the decision-level information fusion fault diagnosis methods, and the data splicing-level information fusion fault diagnosis methods, respectively. The three comparison algorithms are the feature fusion fault diagnosis algorithm based on two-stage Bayesian inference decision fusion (TBIDF), the decision fusion fault diagnosis algorithm based on improved D-S (FDB-IDS), and the data fusion fault diagnosis algorithm based on a long-short term memory network on tree structures (S-LSTM). Figures 10 and 11 show the fault diagnosis accuracy and the F1-Score indicators of different fault diagnosis algorithms. It can be seen from the numerical results that the proposed MLMF-CR algorithm can achieve the highest fault diagnosis accuracy and the F1-Score while the TBIDF, the FDB-IDS, and the S-LSTM algorithms all have different levels of lacking in fault identification accuracy and F1-Score indicators. Among them, the TBIDF algorithm is superior to the FDB-IDS algorithm and the S-LSTM algorithm regarding fault diagnosis accuracy and the F1-Score index in most of the experiments. The highest and lowest recognition accuracy of the TBIDF algorithm in multiple experiments is 98.75% and  
Figure 8: Fault diagnosis accuracy under different information sources. 96.36%, respectively. However, the TBIDF algorithm still has some uncertainties in feature selection and feature fusion, which may impact the final fault diagnosis results. At the same time, in the feature selection and fusion process, some effective state information may be deleted by mistake, leading to a decline in the fusion accuracy. The reasons described above make the TBIDF algorithm have high requirements for the preprocessing of the initial data, which is not as good as the applicability of the proposed algorithm in the industrial operating environment. The fault diagnosis accuracy of the FDB-IDS algorithm in different experiments exceeds 95.13%. In addition, the F1-Score index under different operating statuses also maintains a relatively stable level, illustrating the effectiveness of decision-making fusion to a certain extent. However, the algorithm did not fully consider the timing characteristics of bearing data and the actual background of industrial big data. It directly selects BP neural network, RBF neural network, etc., to build local diagnostic modules, resulting in poor algorithm performance when dealing with large-scale training samples. The S-LSTM algorithm performs generally in terms of fault identification accuracy and F1-Score. The F1-Score values under different operating statuses are 97.14%, 94.14%, and 93.22%, respectively, which show that the fluctuation range under different statuses is relatively obvious. The S-LSTM algorithm combines the bearing vibration and current signals into a fusion signal as the input of the LSTM network. Although such a data splicing and fusion method can minimize the loss of information, it also reduces the fault tolerance of the algorithm and affects the accuracy of fault identification to a certain extent. In addition, the bearing operating state data at a certain moment is related to the state at the previous moment and the state at the subsequent moment. However, the LSTM network ignores the correlation characteristics of the bearing time series data in the time reverse part, which is one of the reasons why the performance of the S-LSTM is not as good as our proposed algorithm. In addition, the time cost required by different algorithms to complete fault diagnosis is also considered, e.g., the operating efficiency of each algorithm.