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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(More)
The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than state-of-the-art methods while still guaranteeing the same outliers. By combining simple(More)
The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the(More)
The worldwide civilian aviation system is one of the most complex dynamical systems ever created. Most modern commercial aircraft have onboard flight data recorders (FDR) that record several hundred discrete and continuous parameters at approximately 1 Hz for the entire duration of the flight. This data contains information about the flight control systems,(More)
In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-per-use packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track(More)
System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side(More)
The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground(More)
On line detection techniques to monitor the health of rotating engine components are becoming increasingly attractive options to aircraft engine companies in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenging feature to easily implement, especially, in the presence of scattered loading conditions,(More)