## 5 Expanding the Transfer Scenarios of the TL-Based Diagnostic Algorithm
### Transfer between Four Operating Conditions  
The target domain predictive accuracies across the twelve possible transfer scenarios, based on the four operating conditions, are shown in Table 4; each row contains a target  
Figure 12: (a) 2D t-SNE visualisation of cases from source and target domain without TL. (b) 2D t-SNE visualisation of cases from source and target domain after TCA. (c) 2D t-SNE visualisation of cases from source and target domain after JDA. domain condition, and each column contains a source domain condition. In Table 4, the highest result(s) under each scenario have also been highlighted. With a minimum predictive accuracy of 88.95% and an average predictive accuracy of 94.89%, it can be concluded that TCA is accurate and reliable in the expanded range of operating conditions and demonstrates good generalisability. JDA has improved the accuracy from the first TCA loop in one of the transfer scenarios, from Ground to 35k ft, where JDA increased the predictive accuracy to 92.82% compared to 92.27% by the TCA loop. This improvement demonstrates the benefit of the predictive accuracy obtained by aligning conditional distributions. However, in general, JDA performs worse than TCA in these expanded transfer scenarios, achieving a lower average predictive accuracy of 78.87% and an accuracy of less than 50% for three of the transfer scenarios. As a result, the diagnostic algorithm based on JDA does not have good generalisability in these cases. The lack of generalisability for JDA on this dataset can be explained in terms of the characteristics of the JDA algorithm. Because the calculation of the target domain conditional distributions is based on pseudo labels rather than true labels, any wrongly predicted pseudo labels will result in a calculation of a conditional distribution that deviates from the true conditional distribution. This design allows JDA to work for target domains with no labels, yet an inevitable flaw would appear when too many pseudo labels are incorrectly predicted in any one of the JDA iterations. Figures 13 and 14 are used to explain such behaviour with one example scenario, where JDA produces a low accuracy at convergence, which is the transfer from 28k ft to Ground conditions. Figure 13a,b plots the predictive accuracy at each iteration, with the 0 th iteration noting the predictive accuracy without TCA or JDA, the 1 st iteration being the TCA loop, and the 2 nd to 20 th iterations being the JDA loops. Figure 13a is plotted for the scenario of interest, and to compare with a transfer scenario were JDA produces a high predictive accuracy at convergence, Figure 13b is plotted for the transfer from 35k ft to 28k ft. It was observed that the accuracy fluctuated only moderately during the JDA iterations. Moreover, most scenarios where JDA achieves high accuracy follow a similar trend, as shown in Figure 13b. Hence, a sharp reduction, such as that found at the 3 rd iteration in Figure 13a, is identified as a unique feature for cases with low JDA accuracy. How it results in low accuracy is described as follows. The sharp reduction in the 3 rd iteration in Figure 13a represents the prediction of many incorrect pseudo labels. The incorrect pseudo labels would have a cascading effect on the consecutive JDA loops and is hard for the accuracy to recover, because the conditional distribution calculated in the next loop is based on the large quantity of incorrect pseudo labels that will make it deviate further from the true conditional distribution. Then, in the next loop, the alignment of the source and target domain distribution will be misled by the incorrectly predicted conditional distribution, which potentially leads to more wrongly predicted pseudo labels at the end of the loop.