Multi-regularized tensor-based framework for identifying hard landings

Hard landings are a significant concern in civil aviation, often resulting in aircraft structural damage, financial losses, and compromised passenger safety. Automating the detection of such incidents faces challenges due to the complexities of Quick Access Recorder (QAR) data, which exhibit multi-channel interdependencies and temporal dynamics. Furthermore, environmental factors tied to flight-variant data, such as the geographic attributes of landing airports, can influence the occurrence of hard landings, yet these factors are often neglected in existing methodologies. This omission limits the practical utility of current approaches for enhancing safety in civil aviation. To address these challenges, we propose a multi-regularized tensor-based framework that models QAR data as a high-order tensor and applies tensor decomposition to extract latent patterns that characterize hard landing scenarios. The model incorporates tailored regularization terms to address both temporal correlations and inter-channel couplings across aircraft systems. To enable efficient computation, we develop a customized Block Coordinate Descent (BCD) algorithm, designed for efficient processing with high-dimensional factor matrices. The effectiveness of the proposed framework is validated using real-world civil aviation data from China, demonstrating superior performance in identifying hard landings.