Decision support system on the grid

Aero-engines are extremely reliable machines and operational failures are rare. However, currently great effort is being put into reducing the number of in-flight engine shutdowns, aborted take-offs and flight delays through the use of advanced engine health monitoring technology. This represents a benefit to society through reduced delays, reduced anxiety and reduced cost of ownership of aircraft. This is reflected in a change of emphasis within aero-engine companies where, instead of selling engines to customers, there is a fundamental shift to adoption of power-by-the-hour contracts. In these contracts, airlines make fixed regular payments based on the hours flown and the engine manufacturer retains responsibility for maintaining the engine. To support this new approach, improvements in in-flight monitoring of engines are being introduced with the collection of much more detailed data on the operation of the engine. At the same time advances have been made in Internet technologies providing a worldwide network of computers that can be used to access and process that data. The explosion of available knowledge within those large datasets also presents its own problems and here it is necessary to work on advanced decision support systems to identify the useful information in the data and provide knowledge-based advice between individual aircraft, airline repair and overhaul bases, world-wide data warehouses and the engine manufacturer. This paper presents a practical framework in which to build such a system that is inherent in the emerging Grid computing paradigm that provides the necessary computing resources. A demonstrator system already developed and implemented in the UK E-Science Grid project, DAME, is introduced.

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