Application of Machine Learning in Flight Safety Event Reasoning
Application of Machine Learning in Flight Safety Event Reasoning
QAR (quick access recorder) data exceedance and statistical analyses is used to flight safety event reasoning. Machine learning algorithms are applied to fully automatically classify pilot behaviors into classes. Both classifications (supervised) and clustering (non-supervised) methods are tried, for instance, gentle Adaboost (Adaptive Boosting) classification, K-means and Gustafson-Kessel (G-K) clustering. Routine exceedance events are assigned to gentle adaboost labels for training model and evaluating testing result, while K-means and G-K clustering are conducted in free-style. All results are compared, and findout unveils that each method emphasizes different series of pilot performances given by a method named Minimum Redundancy Maximum Relevance (mRMR). After theoretical study, we realize in our cases, G-K clustering is much more accurate than K-means in clustering methods.
Document information
Application of Machine Learning in Flight Safety Event Reasoning
2021-10-20
1314190 byte
Conference paper
Electronic Resource
English
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