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Abstract
Health and Usage Monitoring Systems (HUMS) generate a significant amount of data used for on-board and off-board monitoring of the health of the aircraft and its components. When this data is aggregated over the life of an aircraft, it becomes an invaluable resource that enables decision making for diagnostics, prognostics, and fleet management. At the fleet level, the amount of data being ingested, stored, and processed becomes a challenge in itself. The capability to easily handle data of this size is critical to be responsive to time-critical inquiries, iterate on data modeling, and enable efficient diagnostics and prognostics algorithm development. This paper discusses how massively scalable data analytics technologies have been used to enable rapid decision support using HUMS and other data sources. Several use cases are highlighted to show the novel opportunities enabled by these technologies along with associated challenges.

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  Leveraging Massively Scalable Data Analytics Technologies to Enable Rapid HUMS-Based Fleet Management Decision Support

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