# Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
## 4 Experimental Results and Analysis  
In this experiment, this study collected 4754 images of the press-fit curves; among them, the proportion of unqualified images accounts for 2.3% of the total. All the images in this experiment are divided into two categories, qualified or unqualified, by senior on-site inspectors based on their experience. Table 1 shows the number of qualified and unqualified images for the press-fit curve used in training, validating, and testing, respectively.  
To quantitatively evaluate the overall performance of the proposed intelligent system to recognize press-fit curves in railway wheelset press-fit assembly, the following three measurements are adopted: accuracy, precision, and recall. Let TQ, TU, FQ, and FU represent \"true qualified\", \"true unqualified\", \"false qualified\", and \"false unqualified\", respectively, in the confusion matrix, as shown in Table 2.  
The test result of a qualified condition is either qualified (TQ) or unqualified (FQ), while the test result of an unqualified condition is either qualified (FU) or unqualified (TU). The accuracy is the proportion of both true qualified and true unqualified for press-fit curves in all test results. It is the overall correct classification rate of all test results. Precision, also known as precision rate, is the proportion of all qualified test results that are truly qualified press-fit curves. The recall represents the probability of classifying the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Training** & **Validating** & **Testing** & \multirow{2}{*}{**Total**} \\  & **Dataset** & **Dataset** & **Dataset** & **Dataset** \\ \hline
**Qualified images** & 373 & 409 & 3863 & **4645** \\
**Unqualified image** & 26 & 55 & 28 & **109** \\ \hline
**Total** & **399** & **464** & **3891** & **4754** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of experimental datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**Actual Results**} \\ \cline{2-4}  & **Q: Qualified; U: Unqualified)** & **Q** & **U** \\ \hline
**Test results** & **Q** & TQ (3255) & FQ (0) \\
**U** & FU (608) & TU (28) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-relations between test and actual results.  
press-fit curve as a qualified condition if it is truly qualified. The definitions for the above measurements are listed below [44]:  
\[\text{Accuracy}=(\text{TQ}+\text{TU})/(\text{TQ}+\text{TU}+\text{FQ}+\text{FU}), \tag{3}\]  
\[\text{Precision}=\text{TQ}/(\text{TQ}+\text{FQ}), \tag{4}\]  
\[\text{Recall}=\text{TQ}/(\text{TQ}+\text{FU}). \tag{5}\]  
For the classification results of the 3891 press-fit curves in the testing dataset, the TQ, TU, FQ, and FU are 3255, 28, 0, and 608, respectively. The accuracy is 84.37% (\((3255+28)/3891\)), and the results of precision and recall are 100% (\(3255/(3255+0)\)) and 84.26% (\(3255/(3255+608)\)), respectively. Among the three efficiency evaluation indicators, we can see that the precision performance is quite outstanding. The proposed intelligent system can successfully detect all unqualified images. This is a significant indicator of railway equipment manufacturers that strictly control abnormal events during the wheelset production process. The proposed method is rigorous in identifying whether the press-fit curve is unqualified, to avoid the possibility of false qualified (FQ). However, this will increase the probability of false unqualified (FU) results, leading to decreased accuracy and recall. In addition, for safety reasons, if operators want to recheck press-fit images manually, they only need to check the images classified as unqualified by the proposed system, thereby reducing the effort of manual inspection.