Summary:
Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits - the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.
Spanish layman's summary:
Para conseguir mejorar los elevados niveles de seguridad en el sector aeronaútico, es necesario realizar análisis de las operaciones diarias. Este trabajo aplica técnicas de clusting basadas en modelos Gaussianos (Gaussian Mixture Models) sobre datos recogidos en las "Cajas negras" (Flight Data Recorders).
English layman's summary:
To improve the high levels of safety in the aeronautical industry, it is necessary to analyze daily operations to identify new risk factors. This work applies clustering techniques based on Gaussian Mixture Models to aircraft data collected with onboard blackbloxes (Flight Data Recorders).
Keywords: Flight safety; Flight data; Flight operations monitoring; Anomaly detection; Cluster analysis
JCR Impact Factor and WoS quartile: 3,805 - Q1 (2016); 7,600 - Q1 (2023)
DOI reference:
https://doi.org/10.1016/j.trc.2016.01.007
Published on paper: March 2016.
Published on-line: February 2016.
Citation:
L. Li, R.J. Hansman, R. Palacios, R. Welsch, Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transportation Research Part C - Emerging Technologies. Vol. 64, pp. 45 - 57, March 2016. [Online: February 2016]
