# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 6 Conclusions and Future Work  
### Conclusions  
To improve the efficiency of developing fault diagnosis for complex aircraft systems, this work proposed an approach by transferring diagnostic knowledge between vastly different engineering systems. Branches of TL were used with the potential to achieve the transfer between distant and dissimilar domains, and two major directions were attempted, using an analogy or an intermediate domain. Among the three systems of interest, the ECS and APU datasets were selected as the target domain, assuming only a limited number of labelled data was available, and the fuel system was used as the source domain, which was used to pre-train the deep networks. The results of TL-based methods were compared against the results from a 1D-CNN without TL to verify if the knowledge transfer could bring positive benefits for the fault diagnosis. Using this analogy, the optimal order of target domain features that gave the minimum MMD distance with the source domain features was found, which would suggest an analogical relationship between the source and target domain features. After reordering the target domain features this way, the 1D-CNN pre-trained with the fuel system data was transferred to the ECS and APU datasets, where it was finetuned by the small training set and tested by the testing set. The results show that this TL solution based on 1D-CNN improved the accuracy in the ECS case by over 8%, demonstrating the pattern recognition ability transferred from the fuel system to the ECS. However, it did not improve the APU case, which means the positive transfer benefit is not guaranteed where there are no similar patterns between the two datasets. Hence, an alternative method was considered necessary. Using an intermediate domain, the image recognition domain, the TL solution based on 2D-CNN was implemented. Because 2D-CNN pre-trained with a large image dataset existed, two source domains were considered: the ImageNet dataset and the fuel system dataset. The transfer from ImageNet to the ECS and APU cases reduced the minimum labelled cases required to reach a high accuracy, from 27 to 15 and from 40 to 34, respectively. Figure 16: Grad-CAM visualisation plot for the confusing cases: F11 (**left**) and F12 (**right**). \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Method**} & **Average Accuracy** & **Average Accuracy** & **Average Accuracy** \\  & **over Single Fault** & **over Multiple** & **over All Cases** \\  & **Cases Only** & **Fault Cases Only** & **over All Cases** \\ \hline
1D CNN non-TL & 94.00\% & 50.00\% & 72.00\% \\ baseline & 99.33\% & 52.66\% & 76.00\% \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy for APU testing set with 34-case training set. The transfer from fuel system data also demonstrated positive benefits for accuracy. These results prove the possibility of using 2D-CNN-based TL to relieve the requirement of large training datasets. In practical terms, the proposed method provides an accurate and efficient diagnosis solution for systems with data scarcity and modelling difficulties, such as aircraft systems and other complex engineering systems. Additionally, using ImageNet-pre-trained 2D-CNN in place of generating a simulation dataset for complex systems in the source domain considerably relieves the workload of developing fault diagnosis algorithms. Using Grad-CAM to visualise the concentration regions of the ImageNet transferred 2D-CNN when making the predictions, it was found that the concentration regions coincided with the distinct features of each fault mode, which validated the fact that the transfer of pattern recognition ability is the key to improving prediction accuracy compared to the non-TL method. Furthermore, the pattern recognition capacity transferred to the APU case was able to distinguish minute differences between multiple fault modes, which improved the complex fault pattern distinguishing ability.