Research On Interpretability For Hard Landing Incident Of Civil Aircraft Based On Flight Data

Flight safety is the hot topic to civil aviation all the time,and landings are considered to be the most frequent phases of unsafe incidents during the flight.As a classic exceedance of the landing phase,hard landing incident is particularly valued by airlines.Traditional studies on hard landing mostly rely on accident investigation or expert manual analysis,which is inefficient and subjective without data support.Recently,as Quick Access Recorder(QAR)has been widely used,immense flight data is generated,which fully records the real-time dynamic information during the flight.Therefore,it is particularly necessary to study how to utilize advanced technology of big data and artificial intelligence to carry out research on hard landing exceedances based on QAR data.Currently,experts and scholars have studied flight safety issues based on QAR data.However,existing studies face the following three challenges: first of all,most studies depend on expert experience and fail to extract deep and hidden QAR data features;secondly,weak interpretability is the challenge faced by current studies,and it is difficult to explain the causes of hard landing incidents using simple statistical analysis;finally,most studies aim at predicting hard landing incidents with low prediction accuracy.To address the above issues,the thesis focuses on interpretability and conducts research from the following two aspects:In term of the problem of QAR data feature extraction and interpretability,this thesis proposes an automated recognition method for the causes of hard landing based on curve clustering.Specifically,we first extract QAR data parameters’ curve characteristics.According to expert experience,we link the curve characteristics with the causes of hard landing,establishing a two-level hierarchical classification of hard landing incidents.To improve the recognition rate of the causes of hard landing incidents,we extract hard landing curve-level features/patterns through interpolation and resampling.Subsequently,we turn the clustering into a semi-supervised algorithm by incorporating some expert experience and apply it on the curve-level features to automatically recognize the hard landing patterns and causes.Finally,we propose a risk evaluation algorithm on a handful of hard landing samples to discover high-risk flights from normal ones.In term of the problem of interpretability and prediction accuracy,based on the time series characteristics in QAR data,the thesis abstracts the hard landing prediction problem into time series classification problem,and provides Interpretable Multi-Temporal Convolutional Networks(IMTCN)to explain hard landing incidents.Specifically,to fully extract time series data features,we make full use of Temporal Convolutional Networks(TCN)to learn temporal features of each parameters.Meanwhile,based on the idea of Class Activation Mapping(CAM),visualizing feature importance provides strong interpretability for hard landing incidents.One of the contributions in this thesis is that we have strengthened the communication with flight experts,and deeply integrated the professional knowledge into the hard landing analysis.Experimental results on a dataset with 37 943 A320 aircraft flights show that both the curve clustering method and IMTCN model can exhibits good performance in recognizing and predicting hard landing incidents,whose accuracy reaches up to 92.99% and 95.20% respectively,and provides strong interpretability for hard landing incidents.Moreover,it only requires a handful of hard landing samples to discover high-risk flights from tremendous normal landing flights,which is critical for flight safety warnings.The work above provides a novel technical reference for flight safety.