A data analytics framework for anomaly detection in flight operations

Abstract

In the air transport system, there has been a continuous effort to develop policies, tools, and methodologies that increase and standardize safety levels across the entire commercial aviation market, while also enhancing operational efficiency. Furthermore, there is a current focus on proactive approaches for aviation performance management. Within this context, data mining initiatives such as anomaly detection have become more prominent. Dealing with anomalies is a natural step for achieving the goals regarding operational safety and efficiency, as anomalies are often related to hazardous and inefficient operations. In this work, we propose a systematic flight data analytics framework for anomaly detection in flight operations in order to provide a comprehensive and reusable pipeline for model building, application, and explanation. The solution is designed to be applicable to both online and offline regimes and at multiple scales, while also building on domain-expert analysis. We demonstrate the framework applicability in two scenarios of routine flight operations monitoring considering both airline and air traffic management perspectives. In the first one, the framework is applied to aircraft performance data within an unsupervised learning setting with a density-based clustering approach for anomaly detection in landing operations at Minneapolis–Saint Paul International Airport (KMSP). The results are compared with those obtained with exceedance-based methods used in the current practice, revealing the detection of operationally significant anomalies beyond the benchmark. In the second case study, we apply the framework on flight tracking data within a supervised learning setting with the development of an autoencoder classifier for offline anomaly detection in terminal airspace arrival operations at Sao Paulo/Guarulhos International Airport (SBGR). Additionally, supervised learning models are developed for anomaly explanation. The autoencoder classifier was able to detect operationally significant anomalies, while the explanatory models provided novel insights about contributing factors to the anomalies identified. For instance, we learned that anomalous arrival trajectories are more likely to be associated with landing operations on runway 27 under wind scenarios, with an increase in the odds ratio of 62% and 58% for tailwinds and headwinds, respectively. In addition, we also observed a positive association between anomalies and wind gusts situations.