Using Catalyst Mass-Based Clustering Analysis to Identify Adverse Events during Approach

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.

Automated summary

Discovering and mitigating potential risks in advance is crucial for preventing aviation accidents. However, explaining group-scale precursors using existing methods is challenging because of the assumption about the number of risky flights and the reliance on non-domain knowledge. To address this, we propose a novel technique called CMCA, which accurately characterizes group-scale precursors based on energy management performance. By applying CMCA to a combination of labeled and unlabeled flights, we successfully identify precursors with energy management issues, demonstrating the effectiveness of our method. These findings suggest the importance of pilots' energy management awareness in flight safety.