Description: Aircraft Flight Operations Quality Assurance (FOQA) programs are implemented by most of the aircraft operators. Vast amounts of FOQA data are distributed between many computers, organizations, and geographic locations. This project develops methodology for transforming such distributed data into actionable knowledge in application to aircraft health management from the vehicle level to the fleet level to the national level. The distributed data processing methodology provably obtains the same results as would be obtained if the data could be centralized. The data mining methods are efficient and scalable so that they can return results quickly for 10Tb of distributed data. This data mining technology that we call Distributed Fleet Monitoring (DFM) developed in SBIR Phase I satisfies these requirements. The data are transformed into models, trends, and anomalies. The model training and anomaly monitoring are formulated as convex optimization and decision problems. The optimization agents are distributed over networked computers and are integrated through remote connection interface in a scalable open grid computing framework. Though the data and the computations are distributed, they yield provably the same optimal solution that would be obtained by a centralized optimization. DFM feasibility was demonstrated in the problem of monitoring aircraft flight performance from fleet data using large realistic simulated datasets. We demonstrated efficient computation of quadratic optimal solution by interacting distributed agents. The feasibility demonstration successfully recovered aircraft performance anomalies that are well below the level of the natural variation in the data and are not directly visible. The algorithms are very efficient and scalable. Phase I demonstration extrapolates to processing 10Tb of raw FOQA data in under an hour to detect anomalous units, abnormal flights, and compute predictive trends.

Benefits: The developed software architecture and framework support objectives of NASA Aviation Safety Program by enabling aircraft monitoring applications using large distributed data sets. The architecture is open for integration of third party algorithms. It could enable transition of NASA data mining research into practical use in the aviation industry. The architecture is suitable for use in existing and future FAA and NASA portals for safety monitoring of data from multiple airline operators.

One application of Distributed Fleet Monitoring (DFM) technology is to multivariable FOQA monitoring of commercial aircraft fleet. The architecture is most suitable for use in existing and future FAA nation-wide systems for safety monitoring of data from multiple airline operators. The technology allows monitoring of distributed data sets from different airline while keeping the source data private; only pre-processed abstracted data are collected for centralized processing. The unique value of the technology is in fleet-wide monitoring of aircraft performance that allows improving the safety of operation. DFM monitoring of FOQA data can be used for commercial airlines and military aircraft fleets. The software is automated and easy to deploy because it relies on data-driven models. In addition to improving safety, the technology can be used for condition-based maintenance and fuel consumption monitoring. DFM allow to monitor both airframe and propulsion. There are multiple applications in jet and turboshaft engine fleet monitoring in commercial and military fixed wing aircraft and helicopters. Other potential applications are to monitoring turboshaft engines in ground vehicle fleets. DFM technology could be applied to monitoring of energy generation and power distribution systems. This includes fleets of gas turbines used for power generation, wind turbines in wind farms, power distribution transformers and other such equipment.