# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 3 Methodology
### Transfer Learning Problem  
The three systems, the ECS, APU and fuel system, are all accompanied by validated simulation platforms that are capable of generating reliable data under a variety of conditions and degradations. These simulations have enabled the collection of the three datasets summarised in Section 2.4 with the possibility of generating further data if required. In addition, the data generated are well labelled and balanced to facilitate the training of IFD algorithms. However, such simulations are not always possible to derive or economical to implement, which poses challenges to real-world IFD. Hence, this work seeks to explore  
Figure 7: Methodology: overall TL scheme, a solution based on 1D-CNN, and a solution based on 2D-CNN.  
scenarios that more closely resemble real-world engineering systems as a small sample problem where a well-populated, labelled, balanced dataset is not available. In terms of TL domains, the real-world challenge is that there is only a small, labelled dataset available for training in the target domain. In practical terms, after receiving a large quantity of data from a machine, an engineer with expert knowledge would need to manually label the cases, which gives rise to the labelled dataset that can then be used to train an IFD algorithm for the machine. Due to the cost of labelling and difficulty in gathering abundant faulty cases, this labelled training dataset is generally small in real-world applications, hence the assumption in this work.  
The selection of datasets in the source and target domains can be arbitrary, but working with the three datasets available, the natural choice would be to select the fuel system as the source domain since it is a much richer dataset than ECS and APU in terms of the number of cases and health states. Therefore, this work uses the ECS and APU datasets in the target domain, and the real-world IFD challenge is simulated by only assuming that a small subset of the ECS and APU datasets is available for training. Figure 7 illustrates the setting of the problem and datasets in the source and target domains. As the later sections will show, the deep learning method based on the convolutional neural network (CNN) performs better than traditional machine learning (ML) methods; as such, the TL solution is based on CNNs. Thus, the overall TL process would be first transferring a CNN pre-trained by fuel system data or other data. Then, a small subset of the target domain dataset, i.e., 1-to-50 labelled cases from ECS or APU, is used to finetune the transferred CNN for target domain problems. Each run with 1 to 50 training cases in the target domain can simulate a case in reality where very limited training data are acquired for a system. Finally, to evaluate the accuracy of the finetuned CNN, it is directly applied to the entire ECS or APU dataset to reflect how the TL-based IFD solution would perform in predicting the system's health condition after implementation.  
In addition to a small, labelled training set, there are other features of the ECS and APU datasets that add to the challenge for IFD solutions. Regarding ECS, since data are collected from four operating conditions, the 1-to-50 case training set contains a random mix of cases with different conditions. As a result, the distribution of the discrepancy of cases with different conditions and the possibility of encountering unseen conditions in the testing set could add a challenge to IFD solutions. As for APU, half of the dataset involves multiple faults, so the complex fault patterns result in confusion between classes and raise the difficulty of training accurate IFD solutions.