# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 4 Algorithm Performance Analysis and Comparison
### Evaluations on Fault Diagnosis Ability Using Different Algorithms  
Accuracy in identifying faults is a primary concern with diagnostic algorithms. As indicated in Tables 2 and 3, the three algorithms achieve good performance in detecting electrical connection faults and precisely categorizing different modes of faults with over 90% accuracy in average, especially for the algorithms trained on S\({}_{11}\). The GBDT, which makes detection decisions based on the ensemble results from multiple decision trees, produces nearly 100% accuracy with a very low false prediction rate. This demonstrates that machine learning algorithms can be successfully implemented to detect and diagnose electrical connection faults automatically and intelligently, with less manual effort and higher accuracy. As shown in Tables 4-6, the confusion matrixes of the classification results for the three selected algorithms were each calculated separately. The left half of the tables is the confusion matrix of the algorithms trained with S\({}_{11}\), and the right half is the confusion matrix of algorithms trained with S\({}_{21}\). It can be seen that only relatively few testing samples were incorrectly classified as either of the other two modes, which shows that the three classifier algorithms perform well with S-parameters as input features for fault diagnosis. A comparison of algorithms trained with S\({}_{11}\) and S\({}_{21}\) shows that the accuracy of the former is higher. This is because the reflection parameters (S\({}_{11}\)) of the circuit are related to the impedance, and there are some resonant trough points in the curve. The electrical connection fault not only affects the value of reflection parameter but also affects the position of the resonant trough point. The transmission parameters (S\({}_{21}\)) are mainly related to the loss of the circuit, with less effect on the associated resonance point. Accordingly, an electrical connection fault is mainly reflected in the impact on the transmission parameter value, with S\({}_{11}\) being more representative of electrical connection faults. Moreover, detector SVM performs better than LR on S\({}_{11}\) while the opposite is true for S\({}_{21}\). GBDT appears to be the detector, which is more robust and generally applicable under different circumstances, with higher accuracy and lower false detection rate on both S\({}_{11}\) and S\({}_{21}\), as can be seen by inspection of Tables 4-6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**S\({}_{11}\)**} & \multicolumn{3}{c}{**S\({}_{21}\)**} \\ \hline  & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** \\ \hline M1 & 99 & 0 & 1 & 79 & 0 & 21 \\ \hline M2 & 0 & 96 & 4 & 0 & 55 & 45 \\ \hline M3 & 0 & 0 & 100 & 0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix with detailed fault classification result using SVM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **P** & **R** & **F1** & **AC** \\ \hline SVM & 98.41 & 98.33 & 98.34 & 98.33 \\ \hline LR & 97.82 & 97.67 & 97.68 & 97.67 \\ \hline GBDT & 99.67 & 99.67 & 99.67 & 99.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison using different algorithms on S\({}_{11}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **P** & **R** & **F1** & **AC** \\ \hline SVM & 86.75 & 78.00 & 78.14 & 78.00 \\ \hline LR & 92.91 & 91.00 & 91.15 & 91.00 \\ \hline GBDT & 99.35 & 99.33 & 99.33 & 99.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison using different algorithms on S\({}_{21}\). As can be seen by inspection of Tables 4-6, the three classifiers have higher classification accuracy for fault mode 3 without bias, while the other two modes are mis-classified to some extent.