Over the past three decades, the prevalence of dedicated advanced monitoring systems on aircraft has grown tremendously. These systems commonly include a large number of sensors monitoring everything from outside air temperature to gearbox pressures to the health of drivetrain components (via on-board calculated condition indicators). The motivation for monitoring and collecting this data is to enhance safety and enable intelligent decision making about the operation of the aircraft, including optimizing maintenance, maximizing availability, focusing troubleshooting, and enabling proactive and timely support.
The Health & Usage Monitoring System (HUMS) on board most Sikorsky rotorcraft collects several different types of data, such as parametric, event, usage, regime, and mechanical diagnostics data. When this data is aggregated across an entire fleet over all time, the size of the data alone can be difficult to manage, let alone be tractable for extraction of useful and actionable insights in a reasonable time frame or interactive analysis and visualization. When dealing with datasets of this size (which can be hundreds of terabytes and millions of files for a single fleet), there are several difficult problems with ingesting and storing files, batch processing algorithms/analytics, and serving the data to end users. In addition, there are several other datasets, such as flight test data, supply chain, maintenance, safety, operator information, design specs, logistics, and other external data sources, such as global weather and economic information (oil and gas prices), that Sikorsky uses to drive decision making in the enterprise.
Mike Koelemay explores the data platform that Sikorsky has built in recent years using massively scalable tools such as Hadoop, Spark, and Cassandra to enable the ingestion, storage, serving, and processing of aircraft data and shares several use cases showcasing how this technology has enabled decision support using historical fleet data that was previously impractical, as well as the challenges encountered along the way.
Mike Koelemay is a Fellow and Chief Data Scientist in the Chief Data & Analytics Office at Enterprise Operation, Lockheed Martin
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