# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 3 Methodology
### Transfer Learning Solution Based on 1D-CNN  
Since each case in the three datasets is represented by a 1D symptom vector, the natural choice of the IFD algorithm is a 1D CNN or ML classifier. As later sections will show, 1D-CNN outperforms other traditional ML classifiers when solving the problem without TL, so taking 1D-CNN forward to develop a TL solution between different systems is a viable first attempt. However, within the field of IFD, no TL work between distant domains using 1D-CNN has been found; hence, a novel TL method based on 1D-CNN is required to leverage knowledge from the fuel system to ECS or APU. To derive such a method, a branch of TL that is rarely used in engineering applications is considered for its ability to transfer knowledge between seemingly unrelated domains; this is called relation-based TL. For distant source and target domains, relational TL focuses on the structure or relations between data instances and transfers knowledge based on high-level structural commonality in the two domains, thereby bypassing the lack of low-level data similarity between distant domains [6]. A specific approach to relation-based TL that is most relevant to the problem of interest is transfer learning by structural analogy. Thismethod is inspired by how humans are able to make an analogy across different domains and learn in seemingly irrelevant domains via structural similarities [6]. An example of such a high-level structural similarity is shown in Figure 8. Despite the fact that computer viruses and human diseases appear unconnected, humans infect one another with diseases in a comparable manner as computers infect one another with viruses, which can be seen as a structural similarity. Using transfer learning by structural analogy [13] has helped demonstrate how knowledge in diagnosing cardiovascular diseases helps improve the accuracy of diagnosing respiratory tract diseases. Diagnosing disease in the different human systems mentioned above is a similar task to diagnosing the health status of different aircraft systems; the design principle of transfer learning by structural analogy served as a guideline for designing the 1D-CNN TL solution to transfer knowledge from fuel system data to ECS and APU. In the disease diagnosis example in [13], the ultimate goal of transfer learning by structural analogy is finding analogical pairs of words in the two domains of diseases. Then, by treating the words in the analogical pairs from the two domains as equivalents, a diagnosis algorithm trained in one domain can be transferred to another domain to improve diagnostic accuracy. Optimisation in finding the best analogical pairs is achieved by maximising two goals simultaneously: (1) selecting features that are most relevant to label prediction in the source and target domain, respectively, and (2) finding pairs of analogical features to produce maximum structural similarity between the source and target domain [13]. Because data instances from the fuel system, ECS, and APU are clearly different numerically, finding similar patterns in each data instance between the fuel system in the source domain and ECS and APU in the target domain is crucial, which is a similar idea to relation-based TL. To enhance the possibility of finding similar patterns in the source and target data instances, aligning the order of the source and target domain features according to the analogical pairs is a promising approach.