2012 Conference on Intelligent Data Understanding 2012
DOI: 10.1109/cidu.2012.6382196
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Aircraft anomaly detection using performance models trained on fleet data

Abstract: Abstract-This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into a list of abnormaly performing aircraft, abnormal flight-to-flight trends, and individual flight anomalies by fitting a large scale multi-level regression model to the entire data set. The model takes into account fixed effects: flight-to-flight and vehicle-tovehicle variab… Show more

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Cited by 32 publications
(29 citation statements)
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“…In [15] the authors proposed an approach based on a specially designed linear regression model to describe the aerodynamic forces acting on an aircraft. The constructed model accounts for the flight-to-flight and aircraft-to-aircraft variability, which enables the fitting of a single model to the entire dataset.…”
Section: Related Workmentioning
confidence: 99%
“…In [15] the authors proposed an approach based on a specially designed linear regression model to describe the aerodynamic forces acting on an aircraft. The constructed model accounts for the flight-to-flight and aircraft-to-aircraft variability, which enables the fitting of a single model to the entire dataset.…”
Section: Related Workmentioning
confidence: 99%
“…1 A comprehensive archive of safety data is available through the FAA's Aviation Safety Information Analysis and Sharing (ASIAS) 2 repository, which is being mined by researchers to identify anomalous situations for further analysis to detect emerging issues, potentially leading to policy or procedure changes. 3,4,5 Once discovered, these precursors can also be probed in real-time as a component of real-time safety assessment. Similarly, the FAA uses data mining techniques to monitor operational parameters that define Key Performance Indicators (KPI) throughout the NAS to identify issues and modify operations before issues become hazards; 6 EUROCONTROL's Automatic Safety Monitoring Tool (ASMT) serves a comparable purpose in Europe.…”
Section: Introductionmentioning
confidence: 99%
“…Motivated by a wide variety of applications ranging from fraud detection to aviation safety management through the health monitoring of complex net-works, data center infrastructure management or food risk analysis, unsupervised anomaly detection is now the subject of much attention in the data science literature, see e.g. Gorinevsky et al (2012); T. Fawcett (1997); Viswanathan et al (2012). In frequently encountered practical situations and from the viewpoint embraced in this paper, anomalies coincide with rare measurements that are extremes, i.e.…”
Section: Introductionmentioning
confidence: 99%