# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Interpretation of Result
#### 4.5.4 Comparison and Analysis with the Traditional Improved Model  
The fault diagnosis model proposed in this paper is compared with the improved support vector machine method proposed in the cited literature study [26], the optimized BP neural network method proposed in the cited literature study [27], and the single-sensor 1D-CNN model analysis method. The accuracy rate and f1 score are shown in Table 6.  
The cited study [26] proposed a new integrated SVM model, which applied KPCA in the support vector machine model to extract the critical information of the original data  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Accuracy Rate** & **F1 Score** \\ \hline Multi-sensor 1D-CNN model & 99.3\% & 98.95\% \\ Support Vector Machine (SVM) & 63.08\% & 64.49\% \\ BP neural network & 81.1\% & 82.3\% \\ Single-sensor 1D-CNN model & 86.29\% & 87.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy rate and loss function value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Epochs in Model Training** & **Computation Time** & **Time Complexity** \\ \hline
1 epoch & 108 s 14 ms & 104397312 \\
20 epoch & 36 min 4 s 54 ms & 2087946240 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The computational time and complexity of this algorithm during model training.  
set, and the SVM was further trained. In this paper, three vibration signals of the motor were used as the sample set for training and testing, the RBF kernel function was used as the decision function to realize the nonlinear mapping of the fault features, and the genetic algorithm was used as the weight parameter optimization algorithm of SVM. After several training times, the accuracy rate of the model reached 63.08%.  
The cited study [27] proposed an improved BP neural network, which applied the genetic algorithm to optimize the weight and threshold of the BP neural network, and the weight of each neural network node reached the optimum. In this paper, the three-layers network structure of the input layer, hidden layer, and output layer was adopted, the fully connected network was adopted to connect all the layers, and the Sigmoid function was used as the activation function to output its feature mapping. After numerous training and comparison, it was found that the nodes of each layer were set to 6, 12, and 7, and the diagnostic accuracy rate was the highest, 81.1%.  
The single-sensor 1D-CNN fault diagnosis model had the same network structure and training parameters as the multi-sensor 1D-CNN fault diagnosis model aside from its single-channel input. The radial vibration signal of the front bearing was the network input in the single-sensor 1D-CNN fault diagnosis model, and its diagnosis accuracy rate was 86.29%.  
As can be seen from Table 5, the accuracy and F1 score of the proposed fault diagnosis model in this article are 99.3% and 98.95%, respectively. Compared with the improved SVM, the improved BP neural network, and the single sensor convolutional neural network, the accuracy rate of the fault diagnosis model proposed in this paper is improved by 36.22%, 18.2%, and 13.01%, respectively. The F1 score is improved by 34.46%, 16.65%, and 11.05%, respectively. Therefore, the model in this paper has higher diagnosis efficiency and accuracy compared with the improved traditional motor fault diagnosis model.