# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
### The Application of Transfer Learning in Solving MRO Challenges  
To transfer knowledge between different domains, transfer learning (TL) is a commonly known and well proven machine learning technique. Unlike the traditional machine learning approach, where a large quantity of balanced data is required to train an accurate model, TL extracts knowledge from a source domain to boost the learning performance in a target domain, thereby allowing the training of an accurate model where there is a lack of training data problems [6]. In real-world fault diagnosis scenarios, a lack of data is very common. Because machines usually work in their healthy state under various operation conditions, it is difficult and expensive to collect and label enough data for each of its fault states under all possible operating conditions, hence the lack of a training data problem when developing a fault diagnosis algorithm [7]. Therefore, the ability of TL to train an accurate model in these scenarios is believed to be the key to bridging the gap between academic research and the real-world application of fault diagnosis algorithms [8]. Applying TL in fault diagnosis has received much attention in recent years, and numerous examples can be found showing how the application of TL improves the predictive accuracy and robustness for the fault diagnosis of machines with insufficient training data. Owning to the existence of abundant public open-access datasets, bearings and gearboxes have been the most used cases to validate TL-based fault diagnosis algorithms [9]. For example, [PERSON] et al. [10] developed a TL-based deep network that transferred knowledge between three datasets: a motor bearing, shaft support bearing and locomotive bearing dataset, each operating under different conditions. Considering that all data in the target domain were unlabelled, they used half of the target domain data for domain adaptation with the labelled training data from the source domain, which resulted in a TL-based network that gave an average accuracy of 86.3%, the highest among comparison methods. This is a representative study in bearing fault diagnosis, for it shows the capacity of TL to transfer diagnostic knowledge between distinct types of bearings under different conditions simultaneously. The successful transfer from lab-based bearings to locomotive bearings has demonstrated the possibility of using lab experiments, which are cheaper and more controllable for fault injection, to aid the design of fault diagnosis for real engineering assets, which are often expensive and even impossible to collect fault data from. In aerospace-related topics, TL has also been proven useful in improving fault diagnosis models. A few examples were found for the gas path fault diagnosis for aeroengines and gas turbines. For instance, [PERSON] et al. [11] conducted simulations of an aeroengine and collected four datasets under different operating conditions, environmental conditions, and performance degradations to simulate the different distributions of data expected from a fleet of engines. They proposed a TL-based model that improves the accuracy and robustness of fault diagnosis for target domain engines with only unlabelled data, which demonstrated the ability of TL to transfer diagnostic knowledge from data-rich engines to data-poor engines. Another common application is aerospace sensors, such as the Attitude Control System (ACS) on spacecraft. [PERSON] et al. [12] used a TL-based deep network to transfer fault location diagnosis knowledge from a mathematical model of the ACS to a benchtop gyro system representing the real ACS on the spacecraft. Since fault injection can be easily performed in the mathematical model, they were able to generate a fault-rich dataset and use that to pre-train a deep model. Then, by transferring the pre-trained model to the target domain dataset and finetuning it with healthy target cases, significant accuracy improvements were found for predicting the fault states.