Aircraft flight regime recognition with deep temporal segmentation neural network

Timely and effective flight regime recognition is one of the key tasks for structural usage monitoring, it can provide early warning to the dangerous regime. However, existing methods for flight regime recognition either rely on expert knowledge or ignore the continuity and decision boundary of the regimes, which limits their performance for complex regimes and hinders their deployment in practice. In this paper, we provided a brand-new solution for this problem and designed a deep temporal segmentation neural network to realize intelligent regime segmentation. Meanwhile, we revealed the long-tailed distribution of flight regimes and proposed class-wise dynamic group rebalance loss to keep inter-class accuracy balanced. To evaluate the effectiveness of the proposed model, we collected and elaborately annotated plentiful actual flight sorties data, including 11 flight regimes. Test results demonstrated that the model can automatically separate different regimes in a continuous flight sortie without any pre-processing and post-processing while extracting the accurate regime boundary and achieving 95.98% recognition accuracy.