## 5 Expanding the Transfer Scenarios of the TL-Based Diagnostic Algorithm
### Transfer with Different Case Compositions in the Target Domain  
Previous experiments have been carried out with the same case composition in the source and target domain, which consists of 91 healthy cases and 18 cases for each degradation labels including PHX fault, SHX fault, ACM fault, TCV fault, and RI blockage. However, it is unreasonable to expect the same composition of cases when collecting real-life data. Hence, there is a need to test TL-based diagnostic algorithms with different case compositions in the target domain. Five different compositions of the cases in the target domain were simulated, and the details are listed in Table 5. In composition 1, half of all healthy cases are removed, which makes the proportion of faulty cases higher in the target domain than in the source domain. This may not be realistic, because normally an ECS generates abundant healthy cases compared to faulty cases, but it is worth examining if the opposite scenario affects the algorithm. In composition 2, the RI blockage cases with minor and severe degradation levels are removed to leave only one degradation level for RI blockage cases in the target domain, which corresponds to a real-life scenario where faults collected are not in multiple degradation levels. The same operation is done to both RI blockage cases and PHX fault cases in composition 3. In composition 4, all RI blockage cases are removed from the target domain, and in composition 5, all RI blockage cases and TCV fault cases are removed from the target domain. Compositions 4 and 5 simulate scenarios in which data for certain fault modes have not been collected. This increases the distribution discrepancy between the source and target domains, and hence, it tests how the TCA- and JDA-based algorithms handle larger distribution discrepancy. Table 6 shows the predictive accuracies of TCA and JDA. The number in the bracket after each target domain operating condition corresponds to the case composition number introduced in Table 5. Each row in Table 6 contains the predictive accuracy of TCA and JDA in a particular target domain after transferring knowledge from different source domain conditions. Comparing TCA and JDA, the highest result(s) under each scenario have been highlighted. \begin{table}
\begin{tabular}{c l l} \hline
**Cases Composition** & **Scenario Simulated in Target** & **Composition of Cases in Source Domain (S) and Target** \\
**Number** & **Domain** & **Domain (T)** \\ \hline
1 & A fault-rich ECS & S: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\  & T: 46 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\
2 & Less degradation level in 1 fault mode & S: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\  & T: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 6 RI (mediums only) \\
3 & Less degradation level in 2 fault & S: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\  & mode 2 & T: 91 H, 18 ACM, 6 PHX (mediums only), 18 SHX, 18 TCV, 6 RI \\  & & (mediums only) \\
4 & No data for 1 fault mode & S: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\  & T: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 0 RI \\
5 & No data for 2 fault modes & S: 91 H, 18 ACM, 18 PHX, 18 SHX, 18 TCV, 18 RI \\  & T: 91 H, 18 ACM, 18 PHX, 18 SHX, 0 TCV, 0 RI \\ \hline \end{tabular}
\end{table} 
Table 5: Different case composition the target domain. It was found that JDA achieves a higher predictive accuracy than TCA in several transfer scenarios. This improvement in accuracy highlights the advantage of aligning the conditional distribution in addition to aligning the marginal distribution by TCA. However, in general, JDA does not perform as well as TCA, generating an average accuracy of 77.47% compared to 95.22% from TCA. This is partly because by making the target domain case composition different from the source domain, it is more likely to have many incorrectly predicted pseudo labels in one of the JDA iterations. Because of the reason described in Section 5.1, this would eventually lead to a low predictive accuracy for JDA at convergence.