## 6 An Alternative TL-Based Fault Diagnosis Process for Unlabelled Target Domain Using TCA
For example, in the transfer scenario from Ground to 41k ft, despite the alternative method of feeding deviation data instead of nominal data to the k-NN classifier improving the diagnostic accuracy from 54.14% to 61.88%, TCA produced a much higher accuracy of 96.13%. Closer examination into the misclassification details revealed that both non-TL method and the alternative method show confusion between PHX fault and RI Blockage cases, whereas such confusion is not found for TCA, as shown in Figure 16a,c,e. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fault**} & \multicolumn{8}{c}{**Transfer Scenario**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Ground–**Ground–**Ground–**28,000**} & \multirow{2}{*}{**28,000**} & \multirow{2}{*}{**28,000**} & \multirow{2}{*}{**35,000**} & \multirow{2}{*}{**35,000**} & \multirow{2}{*}{**35,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**41,000**} \\
**nostic** & **28,000** & **35,000** & **41,000** & & & & & & & ft– & ft– & ft– \\
**Method** & **ft** & **ft** & **ft** & **Ground** & **35,000** & **41,000** & & & & & 28,000 & **41,000** & **ft–** & **ft–** \\  & \multicolumn{2}{c}{**28,000**} & \multicolumn{2}{c}{**35,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**6 round**} & \multirow{2}{*}{**28,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**6 round**} & \multirow{2}{*}{**28,000**} & \multirow{2}{*}{**41,000**} & \multirow{2}{*}{**6 round**} & \multirow{2}{*}{**28,000**} & \multirow{2}{*}{**35,000**} \\
**kNN** & 60.77 & 33.15 & 54.14 & 25.97 & 60.77 & 96.69 & 44.20 & 74.03 & 89.50 & 47.51 & 95.58 & 82.32 & 63.72 \\ \hline A & & & & & & & & & & & & \\ healthy & & & & & & & & & & & & & \\ base- & & & & & & & & & & & & & \\ line & & & & & & & & & & & & & \\ case & & & & & & & & & & & & & \\ TCA + & & & & & & & & & & & & & \\ kNN & & & & & & & & & & & & & \\ on de- & & & & & & & & & & & & & \\ via & & & & & & & & & & & & & \\ data & & & & & & & & & & & & & \\ \hline TCA & **100** & **92.27** & **96.13** & **88.95** & **93.37** & **97.79** & **90.06** & **94.48** & **100** & **90.06** & 95.58 & **100** & **94.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Target domain predictive accuracy by different approaches to the problem. Figure 16: (**a**) Confusion matrix for non-TL approach using nominal data (Ground–41k ft). (**b**) Plot of cases as nominal data for the confused classes in the target domain. (**c**) Confusion matrix for non-TL approach using deviation data (Ground—41k ft). (**d**) Plot of cases as deviation data for the confused classes in the target domain. (**e**) Confusion matrix for TCA results (Ground—41k ft). (**f**) Plot of cases after applying TCA for the confused classes in the target domain. To investigate how TCA distinguishes confused cases, while other methods do not, the cases for the confused classes in the target domain are plotted in Figure 16. Figure 16b,d show the difference between the nominal data and the deviation data. Because the original temperature profile shows overlapping between the two classes in Figure 16a, taking deviation from a common baseline case cannot resolve the overlapping, as shown in Figure 16d. However, the situation is different after applying TCA. As Figure 16f shows, the two confused classes can be distinguished with the application of TCA. Therefore, TCA has demonstrated its ability to distinguish confused classes in the unlabelled target domain based on labelled source domain data, which gives it better diagnostic accuracy than feeding the k-NN classifier with deviation data.