An Adaptive Online Learning Model for Flight Data Cluster Analysis

Zhao, Weizun; He, Fang; Li, Lishuai; Xiao, Gang

doi:10.1109/dasc.2018.8569600

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…These clustering algorithms focus on anomaly detection for historical flight data, which causes its inability to track the cluster changes in flight. Zhao et al [21] developed an online clustering algorithm to achieve cluster adjustment as onboard flight data update. Because these models do not consider multi-parameter coupling and timevarying characteristics from the perspective of flight safety, it is difficult to accurately describe the relationship between flight state and LOC risk.…”

Section: Classic Clusteringmentioning

confidence: 99%

Flight risk evaluation based on flight state deep clustering network

Wang

Pei

et al. 2023

Complex Intell. Syst.

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Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder–decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below $$6\%$$ 6 % . Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.

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Section: Classic Clusteringmentioning

confidence: 99%