Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data

Abstract

There is a strong and growing interest in using the large amount of high‐quality operational data available within an airline. One reason for this is the push by regulators to use data to demonstrate safety performance by monitoring the outputs of Safety Performance Indicators relative to targeted goals. However, the current exceedance‐based approaches alone do not provide sufficient operational risk information to support managers and operators making proximate real‐time data‐driven decisions. The purpose of this study was to develop and test a set of metrics which can complement the current exceedance‐based methods. The approach was to develop two construct variables that were designed with the aim to: (1) create an aggregate construct variable that can differentiate between normal and abnormal landings (row_mean); and (2) determine if temporal sequence patterns can be detected within the data set that can differentiate between the two landing groups (row_sequence). To assess the differentiation ability of the aggregate constructs, a set of both statistical and visual tests were run in order to detect quantitative and qualitative differences between the data series representing two landing groups prior to touchdown. The result, verified with a time series k‐means cluster analysis, show that the composite constructs seem to differentiate normal and abnormal landings by capturing time‐varying importance of individual variables in the final 300 seconds before touchdown. Together the approaches discussed in this article present an interesting and complementary way forward that should be further pursued.

Aviation Accident and Incident Forecasting Combining Occurrence Investigation and Meteorological Data Using Machine Learning

“…Several examples of AI applications in aviation can be identified from recent literature in which machine learning techniques have been used to develop algorithms for forecasting and preventing aeronautical accidents (Patriarca et al, 2022); detecting normality or anomalies in operations from flight data Stogsdill et al, 2021;Xu et al, 2020); providing support for airport pavement maintenance (Barua & Zou, 2021); forecasting take-off times (Dalmau et al, 2021); predicting the true air and ground speeds during aircraft touchdown ; and defining airport capacity (Choi & Kim, 2021), airport congestion, and arrival delays (Rodríguez-Sanz et al, 2019). However, it is noted that such studies predict occurrences based on target variables that may influence operational flight safety and do not consider real data from accidents or incidents, such as meteorological conditions that can affect such accidents or incidents.…”