The risk identification of streamed flight data based on deep temporal network

Compared with post-flight operations quality assurance, it is preferable to identify the operation risks by monitoring streamed flight data. A risk identification method based on streamed flight data deep learning was studied, and the proposed method was further verified in aircraft approaching flight. A deep temporal network (DTN) was constructed by combining temporal convolutional networks (TCN) with gated recurrent units (GRU). Firstly, the DTN was used to extract the long-term memory and the local time-dependent features based on the offline flight dataset. Secondly, the DTN was simplified to the DTN-R network which is suitable for online anomaly monitoring. The anomaly detection tests based on real streamed flight data show that the DTN-R is able to identify typical anomalies including high speed exceedance (HSE), glide slope deviation (GSD) and late flap setting (LFS), thus achieving the online risk monitoring. By studying different risk features, the DTN can be extended to identify other inflight risks. Providing the score of risk confidence, the DTN-R is helpful to explain the causes of abnormal risks. Furthermore, the proposed methodology provides an effective way of assuring flight safety.