Fuzzy electre model for the characterisation of aeronautical operational risks in the approach and landing phase

One of the significant challenges facing the aviation sector is the management of risks arising from its flight operations, especially in the approach and landing phases, where pilot experience and training are of great importance and where the most significant incidents for air safety occur. Therefore, this paper proposes a model inspired by the structure of a Fuzzy ELECTRE model for managing the operational risks that arise in the approach and landing phases that can lead to safety events. Thanks to the analysis of the literature collected, the management criteria and risk parameters to be taken into account for these two flight phases were shown following air safety manuals such as the International Civil Aviation Organization (ICAO) manual, and where the data obtained was obtained qualitatively thanks to the implementation of surveys with expert pilots, whose information served as the primary input for the characterisation of risks. Following the structure of the proposed model, five (5) reference risk scenarios management were constructed using the previous information, and an analysis of the dominance and discrepancy of a risk scenario vs. the previously established reference scenarios was carried out. Finally, it can be concluded that the proposed model allowed the quantitative-qualitative characterisation for managing the most relevant risks in the approach and landing phases, integrating the expertise of experts in this area.info:eu-repo/semantics/publishedVersio

Hybrid machine learning–statistical method for anomaly detection in flight data

This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving the safety and operation of their fleet. Traditionally, this is performed by checking exceedances in pre-set limits to the flight data parameters. However, this method highlights single events during a flight, making this analysis laborious. The process also fails to establish trends or reflect potential unknown hazards. This research took advantage of machine learning techniques to recognize patterns in large datasets by implementing the local outlier factor (LOF). In order to minimize human input, a statistical approach was adopted to establish the threshold value above which the flights are considered to be anomalous and interpret the scores. This paper shows that LOF quantifies the degree of outlier-ness of an outlier rather than binary categorizing a point into inlier or outlier, as in the case of clustering algorithms. Thus, with LOF, for the first time, we demonstrated that in the aviation industry, anomalous flights could not only be identified but also be given an anomaly score to compare two anomalous flights in an unsupervised manner. Furthermore, LOF helps to track anomalous behavior in time during the flight. This is insightful when a flight is abnormal, only for some seconds or short duration. For the first time, we attempted to detect flight parameters responsible for anomalous behavior or at least give direction to human experts looking for the cause of abnormal behavior. This was all analyzed with real-life flight data in an unsupervised manner in contrast to simulated data.peer-reviewe

An incremental clustering method for anomaly detection in flight data

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.Aerospace Transport & Operation