# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 3 Methodology
### Transfer Learning Solution Based on 2D-CNN  
#### 3.3.1 From Image Classification to Fault Diagnosis  
Another approach to achieving cross-system TL is based on 2D-CNN. This is a common approach found in IFD applications, such as [21; 22; 23], which essentially exploit the pre-trained 2D-CNN, using the ImageNet dataset, to improve the fault diagnosis accuracy. Despite the fact that there is no relevance between the images in ImageNet and the signal images for IFD, since 2D-CNN treats the IFD problem in the same way as image classification, knowledge can be transferred from a very distant source domain such as ImageNet. Not limited to using ImageNet as the source dataset, [PERSON] et al. [24] transferred 2D-CNN between two different chemical processes and reported improvement in diagnosis accuracy as a result. Therefore, using image classification as the media, TL based on 2D-CNN is capable of transferring knowledge between distant domains that can be represented by images. This approach can be considered transitive transfer learning (TTL), a branch of TL that transfers knowledge through one or more intermediate domains, bridging the large gaps between the source and target domain [6]. The general idea of TTL is shown in Figure 9. For distant domains, such as the fuel system and ECS or APU, the common factor is that both systems have fault patterns, which can be represented graphically. Thus, the intermediate domain can be achieved through image classification, where 2D-CNN can be transferred from the fuel system to the ECS or APU. In summary, a cross-system TL solution based on 2D-CNN is a justified approach for the problem based on existing IFD applications and the TTL framework.  
The process of implementing a cross-system TL solution based on 2D-CNN is illustrated in Figure 7. Each case in the dataset is converted from symptom vectors to input images into 2D-CNN. Both the fuel system dataset and ImageNet dataset are considered the source domain, and a comparison was made between them to determine which source domain performed better for the problem. The pre-trained CNN from the source domain was transferred to the target domain, where the small ECS or APU training set was used to finetune the top layers before testing on the whole dataset. Several popular deep networks were tested initially, and the best-performing model, ResNet101, was chosen as the base model for the 2D-CNN. Top layers with customised output label spaces were added to the base model to fit the different number of classes in the three systems, as shown in Table 2.  
Figure 9: An illustration of TTL [6].  
It is worth noting that while the TL solution based on 2D-CNN enables cross-system transfer from the fuel system to ECS and APU, an additional benefit it brings is the possibility of using ImageNet as the source domain. Since ImageNet is an incredibly rich dataset with over 14 million images [25], using the pre-training weight from ImageNet provides a highly accurate and reliable CNN. The existence of established weights for most common CNN models also saves on pre-training computational costs. In this work, the ResNet101 model with ImageNet pre-training weight was taken from [PERSON] et al. [26].