# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
## 4 Results and Discussion  
### CNN Classification Result  
In this experiment, two CNN structures were established for defect detection and classification. The structures of both CNN processes are presented in Figure 7. For the 2D CNN, the input data were a set of labeled spectrogram images. The size of the input image was 100 \(\times\) 100 pixels. The first convolution layer contained 11 filters with a size of 3 \(\times\) 3. The second convolution layer contained 10 filters with a size of 3 \(\times\) 3. After the convolution layers, a fully connected layer was added. For the 2D CNN, 70% of the labeled data were used to train the network, and 30% of the data were used as the testing samples.  
Figure 6: Plot of the reconstructed data sets for the drill bits with different health conditions.  
Figure 7: Structures of the 2D CNN and 1D CNN defect diagnosis processes.  
For the 1D CNN structure, the first convolution layer contained 20 filters with a kernel size of 51 and was followed by four sub-sampling layers. The second convolution layer contained 10 filters with a kernel size of 21 and two sub-sampling layers. A fully connected layer was added with four classification outputs.  
The accuracy of the classification results was calculated based on the equation below:  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}\times 100\% \tag{5}\]  
where _TP_, _TN_, _FP_, and _FN_ represent true positive, true negative, false positive, and false negative, respectively. _TP_ indicates that the positive training data are evaluated as positive; _TN_ indicates that the negative training data are evaluated as negative; _FP_ indicates that the negative training data are evaluated as positive; and _FN_ indicates that the positive training data are evaluated as negative.  
Figure 8 presents the training and validation results of the dill bit defect diagnosis using the CNNs. Figure 8a shows the confusion matrix of the final learning results. As the confusion matrix shows, the trained model accurately distinguishes defect #1 and the healthy drill bit from the other drill bits. The overall test accuracy is 97.6%. Such high accuracy is considered to be due to the unique vibration patterns caused by the notched edge in the drill bit.  
Drilling is one of the typical rotating processes; an inherent vibration can be observed during the operation. When drilling with a notched drill bit, the notch will create special vibration patterns along the turning blade. Such vibration patterns should be able to be distinguished relatively easily [26]. Therefore, it can be observed that defect #1 has a high diagnostic accuracy. For the blunt edge (defect #2) and the blunt head (defect #3), both types of defects increase the drilling resistance during the operation. These defects can be considered as adding loadings to the motor. Several studies have reported how vibration patterns are affected by adding loadings to a motor [27; 28]. However, compared to adding loadings to a motor directly, the loadings added to a drill bit with a blunt blade should be relatively small. Therefore, it causes a diagnostic error as reflected in the confusion matrix.  
In the 1D CNN, the overall accuracy is about 96.4%. The confusion matrix is shown in Figure 8b. The 1D CNN also shows a high classification accuracy. The classification results of the 1D CNN are similar to those of the 2D CNN. Similar to the 2D CNN results, there is a misjudgment in defect #2 and defect #3. This indicates that the vibration patterns are similar between the blunt edge and blunt head defects. The classification accuracy of each class is 100% for defect #1 and the healthy drill bit, and 93% for defects #2 and #3.  
Figure 8: (**a**) The confusion matrix of the 2D CNN and (**b**) the confusion matrix of the 1D CNN.