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GE is a world leader in the manufacture of commercial jet engines, offering products for many of the best-selling commercial airframes. With more than 33,000 engines in service, GE Aviation has a history of developing analytics for monitoring its commercial engines fleets. In recent years, GE Aviation Digital has developed advanced analytic solutions for engine monitoring, with the target of improving detection and reducing false alerts, when compared to conventional analytic approaches. The advanced analytics are implemented in a real-time monitoring system which notifies GE’s Fleet Support team on a per flight basis. These analytics are developed and validated using large, historical datasets.
Analytic tools such as SQL Server and MATLAB were used until recently, when GE’s data was moved to an Apache Spark environment. Consequently, our advanced analytics are now being migrated to Spark, where there should also be performance gains with bigger data sets. In this talk we will share experiences of converting our advanced algorithms to custom Spark ML pipelines, as well as outlining various case studies.
With Honor Powrie and Peter Knight