# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 5 Practical Fault Diagnosis Application  
According to the concept of deep learning and the cited literature study [28], after the fault diagnosis model reaches the optimal convergence state through massive data training, blind data sets (unlabeled data sets) must be used to verify its diagnostic capability. In this section, the monitoring system data set (100 data of each fault are collected without labels) is input into the trained fault diagnosis model to verify further the feasibility and effectiveness of the model in the practical application of fault diagnosis. At the same time, the diagnosis results of the monitoring system data set are obtained and analyzed.  
The confusion matrix of monitoring system data sets is shown in Figure 9, where the row represents predicting class (i.e., the output class), the columns represented the real class (i.e., the target class), and the diagonal line corresponds to the correct classification observations, and the off-diagonal corresponded to the misclassification observations, and the lower right corner of the cell showed the overall diagnostic accuracy rate, and the most right column was the precision and false discovery rate, and the bottom row was recall rate and false negative rate [29]. Each cell showed the percentage of the total observation number and frequency. From Figure 9, one sample was wrongly identified as other faults, respectively, in class 1, 3, 4, 5, and 6 faults. In contrast, the identification of other types of faults almost exactly corresponds to the real label with an accuracy rate of 99.3%.  
To observe the effect of diagnostic classification more intuitively, the output data of the fully connected layer in this model is taken out, the data are all reduced to two-dimensional and three-dimensional data by T-SNE, and then the clustering scatter diagram of classification results is drawn [30]. The 2D clustering scatter plot and the 3D clustering scatter plot are shown in Figures 10 and 11, respectively. From the figures, the predicted values of each category are distinguished by different colors, the data points of the same color are concentrated in the same area, the data points of different colors are not mixed, and the distances are large. The classification results are accurate, which further showed that the method had a significant effect on motor fault diagnosis.  
Figure 10: Two-dimensional cluster scatter diagram.  
Figure 9: Confusion matrix.  
To sum up, the model has an excellent diagnostic effect in the actual application scenario of motor fault diagnosis. In the specified position of the motor, workers can install all kinds of sensors, and the multi-source sensor signals during the motor operation can be collected and inputted into the model proposed in this paper to realize the online fault diagnosis of the motor. In addition, to prevent possible misclassification of the model, a variable will be added when the model is deployed in real-time, which will store ten motor fault diagnosis results. When there are more than eight of the same diagnosis results, the diagnosis result will be output to the upper computer, and this variable will be cleared.