Implementing Deep Learning for comprehensive aircraft icing and actuator/sensor fault detection/identification

Abstract

The detection and identification for aircraft icing and actuator/sensor fault has been a lasting topic in flight safety researches. The current algorithms are usually tailored for some specific cases (faults/icing locations, magnitude, etc.). Although the performance of the algorithm in the designated cases may be good, the transferring of it to other different cases is usually heavy as parameters tuning or even algorithm redesigning may be required. In this paper, the author advocates exploring a comprehensive scheme that balance both good performance and wide transferability for different cases. Referring to the current advances in other research communities, we follow the state-of-art Deep Learning (DL) and transfer learning (TL) concepts. A scheme for the icing/actuator fault detection using the DL technique is firstly constructed. The TL is then adopted to transfer this scheme to other different tasks, e.g. fault/icing identification, sensor fault detection. Test results show that the TL-enhanced DL scheme exhibits not only good performance for the designated detection task, but also reflects flexible transferability at low tuning efforts. Via this paper the author advocates furtherly exploring the potentials of the novel DL and TL technique as to advancing the researches/techniques in the flight dynamics and control realm.