## 6 An Alternative TL-Based Fault Diagnosis Process for Unlabelled Target Domain Using TCA
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**TCA:**} & \multicolumn{4}{c}{**TCA:**} \\  & \multicolumn{4}{c}{**TPR on Healthy Cases (\%)**} & \multicolumn{4}{c}{**PPV on Healthy Cases (\%)**} \\ \cline{2-9}  & \multicolumn{4}{c}{**SOURCE**} & \multicolumn{4}{c}{**SOURCE**} \\ \cline{2-9}  & **Ground** & **28,000 ft** & **35,000 ft** & **41,000 ft** & **Ground** & **28,000 ft** & **35,000 ft** & **41,000 ft** \\ \hline Ground (1) & - & 100.00 & 100.00 & 100.00 & - & 69.70 & 71.88 & 75.41 \\ \hline Ground (2) & - & 100.00 & 100.00 & 100.00 & - & 85.85 & 88.35 & 88.35 \\ \hline Ground (3) & - & 65.93 & 100.00 & 100.00 & - & 80.00 & 88.35 & 91.00 \\ \hline Ground (4) & - & 100.00 & 100.00 & 100.00 & - & 89.22 & 93.81 & 93.81 \\ \hline Ground (5) & - & 100.00 & 100.00 & 100.00 & - & 100.00 & 95.79 & 93.81 \\ \hline
28,000 ft (1) & 100.00 & - & 100.00 & 100.00 & 100.00 & - & 85.19 & 88.46 \\ \hline
28,000 ft (2) & 100.00 & - & 100.00 & 100.00 & 100.00 & - & 100.00 & 97.85 \\ \hline
28,000 ft (3) & 96.70 & - & 100.00 & 100.00 & 100.00 & - & 98.91 & 96.81 \\ \hline
28,000 ft (4) & 100.00 & - & 100.00 & 100.00 & 100.00 & - & 100.00 & 97.85 \\ \hline
28,000 ft (5) & 100.00 & - & 90.11 & 100.00 & 100.00 & - & 100.00 & 100.00 \\ \hline
35,000 ft (1) & 100.00 & 86.96 & - & 100.00 & 93.88 & 81.63 & - & 100.00 \\ \hline
35,000 ft (2) & 96.70 & 100.00 & - & 100.00 & 97.78 & 95.79 & - & 100.00 \\ \hline
35,000 ft (3) & 87.91 & 95.60 & - & 100.00 & 97.56 & 94.57 & - & 95.79 \\ \hline
35,000 ft (4) & 92.31 & 100.00 & - & 100.00 & 97.67 & 95.79 & - & 96.81 \\ \hline
35,000 ft (5) & 96.70 & 100.00 & - & 100.00 & 98.88 & 93.81 & - & 97.85 \\ \hline
41,000 ft (1) & 100.00 & 100.00 & 100.00 & - & 95.83 & 86.79 & 97.87 & - \\ \hline
41,000 ft (2) & 100.00 & 100.00 & 100.00 & - & 100.00 & 96.81 & 100.00 & - \\ \hline
41,000 ft (3) & 90.11 & 100.00 & 100.00 & - & 100.00 & 94.79 & 97.85 & - \\ \hline
41,000 ft (4) & 98.90 & 100.00 & 100.00 & - & 100.00 & 97.85 & 100.00 & - \\ \hline
41,000 ft (5) & 100.00 & 100.00 & 100.00 & - & 100.00 & 97.85 & 100.00 & - \\ \hline \multicolumn{9}{c}{Average: 98.30} & \multicolumn{4}{c}{Average: 94.56} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The predictive accuracy and precision of healthy cases in the target domain by TCA. By applying this alternative solution to the transfer between the four operating conditions, the predictive accuracy was recorded, as shown in Table 8. The predictive accuracy following the non-TL approach (kNN) and TCA are also recorded in Table 8 for comparison. The non-TL approach (kNN) has adopted PCA instead of mRMR as the dimensionality reduction technique to align with TCA. Compared with the non-TL approach, the alternative method using deviation data produces higher predictive accuracy in most of the scenarios tested, and the average accuracy shows an improvement from 63.72% to 72.70%. Because k-NN is applied for label prediction in a similar way for the two methods, with the only difference being whether deviation data from a healthy baseline are used, this improvement in accuracy justifies how the designed alternative method effectively reduces the domain shift, alleviating the problem illustrated in Figure 8. Compared with TCA, the alternative method also achieved higher accuracy in one transfer scenario. This proves that taking the deviation data from a healthy baseline case is indeed a useful way to reduce the influence of the domain shift and improve the predictive accuracy for ECS data under different operating conditions. In general, the alternative method did not perform better than TCA, as shown in Table 8. TCA still produced higher predictive accuracy in most transfer scenarios, and the average accuracy of 94.89% also outperforms the 72.70% accuracy by the alternative method. The reason for this is that, although taking the deviation data from a healthy baseline case reduces the influence of domain shift, it cannot effectively distinguish similar cases under different classes.