Fault diagnosis across aircraft systems using image recognition and transfer learning

With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be trained, which is difficult and expensive to obtain for aircraft systems. One solution is to transfer the diagnostic knowledge from one system to another. To achieve this goal, transfer learning was explored, and two approaches were attempted. The first approach uses relational similarity between the source and target domain features to enable the transfer between two different systems. The results show it only works when transferring from the fuel system to ECS but not to APU. The second approach uses image recognition as the intermediate domain linking the distant source and target domains. Using a deep network pre-trained with fuel system images or the ImageNet dataset finetuned with a small amount of target system data, an improvement in accuracy is found for both target systems, with an average of 6.90% in the ECS scenario and 5.04% in the APU scenario. This study outlines a pioneering approach that transfers knowledge between completely different systems, which is a rare transfer learning application in fault diagnosis.